Enhancing skin lesion classification using a Tri-Path Attention Stacked Ensemble architecture with Cohen’s Kappa Proportioned Averaging

Dataset description

This study utilized a publicly available dermatoscopic dataset to ensure a comprehensive and diverse evaluation of skin lesion classification performance.

The dataset, Human Against Machine (HAM10000), was collected from the Harvard Dataverse repository. ⁴⁹ It comprises 10,015 carefully curated dermatoscopic images in JPG format, categorized into seven distinct classes.

The seven lesion categories included in the dataset are Melanoma (MEL), Nevus (NV), Vascular Lesions (VASC), Actinic Keratosis (AK), Basal Cell Carcinoma (BCC), Benign Keratosis (BKL), and Dermatofibroma (DF). Among these categories, MEL, AK, and BCC are classified as malignant lesions, whereas NV, BKL, and DF are considered benign. Certain types of VASC may also demonstrate malignant characteristics.

Table 1 summarizes the dataset distribution, providing a concise overview of the data composition used in this research.

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Table 1.

Brief information of the HAM10000 dataset.

Images	Format	Classes	Source
10015	JPG	7	Harvard Dataverse

Figure 1 presents representative examples from each class, illustrating one sample per category. The pronounced class imbalance within the dataset is further demonstrated through the class distribution visualization in Figure 2.OPEN IN VIEWER

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Figure 2.

Sample distribution for each class in the HAM10000 dataset.

The dataset was carefully preprocessed to meet the requirements of our study. Additional details regarding the exact versions used can be found in HAM10000. ⁵⁰

Methodological approach

The methodological framework of this study started with dataset acquisition, followed by comprehensive data preprocessing. The datasets were subsequently divided into two primary subsets: a main training set and an independent testing set. The independent testing set was completely held out during training and validation, providing truly unseen data for final evaluation.

To mitigate class imbalance, four distinct data augmentation strategies were employed:

• No Augmentation (NA): Only the original dataset was used, without generating any synthetic images.

The most effective augmentation strategy was identified by training a customized network based on EfficientNetV2 variants, followed by evaluation on the independent testing set to determine performance on entirely unseen data.

Next, the data was processed within the Tri-Path Attention Stacked Ensemble (TASE) framework. TASE combined architectures trained on the training set and validated on the validation set. It incorporated models using three Triple Attention (TA) configurations, which included Soft Attention Integration, Channel Attention Integration, and Squeeze-Excitation Attention Integration: TASE: Independent TA, TASE: Serial Stacked TA, and TASE: Parallel Stacked TA.

Predictions from each model were then fused using the Multi-Layer Cohen’s Kappa Proportioned Averaging (ML-CKPA) method, applied across multiple layers to boost performance. This ensemble technique enabled optimal weighting of predictions and improved generalization.

For interpretability, Grad-CAM visualizations were employed, providing insights into model behavior by highlighting critical regions of the input images. A schematic of the sequential steps in this methodology is presented in Figure 3.OPEN IN VIEWER

Preprocessing and data augmentation

To prepare the dataset for effective training, images were first grouped according to their lesion IDs to ensure proper organization at the lesion level. This grouping strategy was explicitly used to enforce lesion-level separation during dataset splitting, preventing any images from the same lesion appearing across different subsets. Careful sampling was then conducted to create distinct subsets for training, validation, and testing. Specifically, 15% of the images were allocated to the independent testing set, while the remaining 85% formed the primary training set. The independent testing set was strictly separated prior to any augmentation process and was fully preserved as unseen data for final evaluation.

Lesion-level separation was strictly enforced during dataset splitting, ensuring that images from the same lesion did not appear across training, validation, or independent testing sets. Furthermore, data augmentation was applied strictly after the splitting process and only to the training set, while the validation and independent testing sets were kept entirely unchanged. This strategy prevents any form of data leakage and ensures a fair and reliable evaluation of the proposed models.

Figure 4 depicts the four data augmentation strategies implemented to address class imbalance:

• No Augmentation (NA): Only the original dataset was used, without generating any synthetic images.

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Figure 4.

Illustration of four data augmentation strategies.

To address class imbalance, roughly 8,000 synthetic images were generated for each class. The primary training dataset was subsequently split into training, validation, and testing subsets in a 70:15:15 ratio, respectively.

Augmentation was carried out using TensorFlow’s ImageDataGenerator, following a comprehensive strategy to increase dataset diversity and enhance model generalization. The process began with contrast enhancement of the original images to improve visual clarity. Various transformations were then applied, including random rotations up to 180 degrees, horizontal and vertical flips, width and height shifts of up to 10%, and zoom adjustments within a 10% range. To fill gaps resulting from these transformations, the nearest neighbor fill mode was utilized, ensuring consistency across generated images. This augmentation strategy simulated a wide variety of variations, effectively improving the robustness of the deep learning model.

Figure 5 presents examples of original, contrast-enhanced, and augmented images, illustrating a sample from the Actinic Keratosis (AK) class along with its augmented variants.OPEN IN VIEWER

Tables 5 and 6 show the comparison of all augmentation strategies in both testing and independent testing data. Accordingly, all primary performance comparisons and conclusions in this study are drawn based on results obtained using the TA strategy.

Development of Tri-Path Attention Stacked Ensemble (TASE) architectures

The TASE framework utilized customized EfficientNetV2 models, fully leveraging Transfer Learning. Specifically, seven pre-trained architectures, including various EfficientNetV2 variants with input dimensions of 299x299x3 and 224x224x3, were fine-tuned. Since these models were originally trained on unrelated datasets, fine-tuning allowed adaptation to our dataset, enabling the extraction of both shallow and deep features effectively. To further improve performance, Triple-Attention (TA) was incorporated in three configurations: Serial Stacked, Parallel Stacked, and Independent Attention. A schematic of the complete architecture is illustrated in Figure 6.OPEN IN VIEWER

The integration process started by importing pre-trained models from the tensorflow library and adapting them to match our specific input dimensions. Outputs were reshaped into a three-dimensional tensor (None, height, width, channels) to align the custom architecture with the pre-trained models for smooth feature extraction.

Three customized CNN architectures incorporating TA were developed:

1. Soft Attention Integrated Network (SAIN): Targeted fine-grained spatial features.

The TA modules were selectively integrated into these networks. For SAIN and SEAIN, TA modules were inserted after each convolutional block, while CAIN incorporated channel attention after every Conv2D layer. This strategic placement balanced computational efficiency and ensured meaningful feature enhancement.

The convolutional backbone consisted of two convolutional blocks, each containing four Conv2D layers with kernels of varying sizes (7x7, 5x5, 3x3, 1x1). The first block used 128 filters, and the second block used 256 filters, with BatchNormalization and MaxPooling2D layers applied for feature refinement and dimensionality reduction. All convolutional layers utilized ReLU activation to prevent vanishing gradient issues.

The three TASE configurations—Serial Stacked, Parallel Stacked, and Independent Attention—are described as follows.

Justification of the proposed architecture and improvements over state-of-the-art methods

Most existing state-of-the-art methods are not specifically designed for skin lesion analysis; instead, they are primarily developed and pre-trained on large-scale datasets such as ImageNet. Therefore, the first key improvement of the proposed framework lies in its domain-specific fine-tuning, where the models are explicitly adapted for skin lesion classification tasks. Moreover, beyond fine-tuning, many existing approaches do not fully exploit advanced attention mechanisms. In contrast, this work incorporates three distinct attention modules—Channel Attention, Squeeze-and-Excitation Attention, and Soft Attention—each contributing uniquely to improving feature representation and model performance. These attention modules are further utilized in three different stacking strategies: Serial (SSA), Parallel (PSA), and Independent (ISA), enabling a more comprehensive and effective feature extraction process compared to conventional methods. Unlike typical state-of-the-art approaches that rely on a single model, this study recognizes that a single architecture may be insufficient for achieving high performance due to risks such as bias and overfitting. To address this limitation, multiple models are combined in both pre-training and post-training stages. Specifically, attention-based stacking is applied before training, while post-training integration is performed using the proposed Cohen’s Kappa Proportioned Averaging (CKPA) ensemble method. This multi-stage integration enhances robustness, reduces bias, and improves overall generalization performance.

Furthermore, each model within the proposed framework was trained using distinct strategies corresponding to the respective stacking architectures. For the Independent Stacked Architecture (ISA), each model was trained separately to ensure independent feature learning. In contrast, for the Serial Stacked Architecture (SSA), models were trained sequentially in a stacked manner, allowing progressive refinement of learned representations. For the Parallel Stacked Architecture (PSA), models were trained concurrently using a parallel stacking approach to capture diverse feature interactions. To ensure stable convergence and prevent overfitting, early stopping was employed during training. Although the maximum number of training epochs was set to 120, the training process was automatically terminated once convergence was achieved, resulting in efficient and optimized model performance.

Feature extraction process

In our methodology, the TASE models were employed for effective feature extraction. The top fully connected layers were excluded (include_top=False), and global average pooling was applied (pooling=’avg’). The resulting outputs were reshaped to optimized dimensions suitable for further processing with additional convolutional layers. These convolutional layers used a variety of filter sizes (7x7, 5x5, 3x3, and 1x1) and incorporated ReLU activation along with batch normalization to stabilize and enhance learning. Max pooling layers were then applied to reduce spatial dimensions, sharpening feature focus. Finally, the extracted feature maps were flattened and passed through fully connected layers with ReLU activation, concluding with a dense output layer using softmax activation to generate class probability predictions. The feature extraction process can be mathematically represented as:

F = ϕ ( X ; θ ) , F ∈ R H × W × C

(1)

The final classification probabilities are computed using the softmax function:

y ^ i = exp ( z i ) ∑ j = 1 K ⁡ exp ( z j )

(2)

Feature visualization

Figure 7 illustrates the hierarchical feature extraction process within an Optimized InceptionV3 architecture. The figure shows activation maps at multiple stages of the transfer learning model, with each row corresponding to a different layer’s activations, providing a detailed view of the progressive transformation of input images:

• Input Layer (input_1): Displayed the preprocessed input image, representing raw pixel data.

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Figure 7.

Feature extraction process illustrated by activation maps (sample visualization).

The visualization in Figure 7 illustrates up to five filters per layer using the viridis colormap, ensuring clarity and effective contrast. These activation maps provide a comprehensive view of how the model hierarchically processes input images, capturing critical features at each stage.

Demonstrated on a single sample and selected layers, this process highlights the systematic extraction of thousands of feature representations. These detailed features substantially enhanced overall model performance by providing deeper insights into how hierarchical patterns were captured across the architecture.

Triplet-Attention (TA)

To improve the model’s ability to focus on important input features while minimizing less relevant information, we employed three complementary attention mechanisms, collectively called Triplet-Attention (TA). This method integrates Channel Attention Integration (CAI), Squeeze-Excitation Attention Integration (SEAI), and Soft Attention Integration (SAI) to efficiently capture and emphasize critical patterns within the data. ⁵¹

Soft Attention Integration (SAI)

The Soft Attention Integration (SAI) module emphasizes assigning attention weights to individual elements of the input, enabling the model to prioritize regions according to their importance. ⁵² The attention mechanism can be expressed as:

a i = exp ( e i ) ∑ j = 1 T ⁡ exp ( e j ) ,

(3)

Here, a _i denotes the attention weight for the i-th input element, T is the total number of input elements, and e _i represents the relevance score of the i-th element. ⁵³

By assigning greater weights to the most important regions, the SAI module directs the model’s focus toward the most relevant portions of the input, thereby improving overall performance. ⁵⁴

Channel Attention Integration (CAI)

The Channel Attention Integration (CAI) module emphasizes the significance of key channels within feature maps by computing attention weights across them. These weights are determined using statistical properties, such as the mean and standard deviation, of the input feature maps and are applied to enhance relevant features. ⁵⁵ The functionality of the CAI module can be expressed mathematically as:

w c = σ W 2 δ W 1 x ,

(4)

y c = w c ⊙ x ,

(5)

Here, x denotes the input feature maps of size C× H× W, W ₁ and W ₂ are learnable weight matrices, δ represents the ReLU activation function, σ is the sigmoid activation function, w _c corresponds to the computed channel attention weight, and ⊙ indicates element-wise multiplication. ⁵⁵

Squeeze-Excitation Attention Integration (SEAI)

The Squeeze-Excitation Attention Integration (SEAI) module emphasizes channel-wise attention, allowing the model to dynamically recalibrate feature maps. ⁵⁶ The module carries out two main operations: aggregation of global spatial information and recalibration of features across channels. For an input feature map x of size C× H× W, the SEAI operations can be expressed as:

z = G l o b a l A v g P o o l i n g ( x ) ,

(6)

s = R e L U ( W 2 ⋅ s i g m o i d ( W 1 ⋅ z ) ) ,

(7)

y = s ⊙ x .

(8)

Here, GlobalAvgPooling performs global spatial information aggregation, and W ₁ and W ₂ are learnable weight matrices. This mechanism allows the model to focus on the most informative channel-wise features within the input data. ⁵⁷

Cohen’s Kappa Proportioned Averaging (CKPA)

We proposed a novel ensemble learning method called Cohen’s Kappa Proportioned Averaging (CKPA), which assigns optimal weights to predictions from multiple classifiers and combines them through weighted averaging. Unlike other metrics, CKPA leverages Cohen’s Kappa to evaluate the agreement between model predictions and the true labels, beyond what is expected by chance. Classifiers with higher Kappa scores are considered more reliable, as they demonstrate stronger consistency with the ground truth labels. By proportionally weighting classifiers according to their Kappa values, the CKPA method enhances both prediction quality and robustness. While CKPA introduces a reliability-aware weighting mechanism based on agreement beyond chance, it is not intended as a universal theoretical replacement for existing ensemble weighting strategies. Unlike calibration- or validation-based methods, CKPA prioritizes consistency between predictions and ground truth through Cohen’s Kappa.

However, agreement-based weighting may favor dominant class patterns in imbalanced datasets. To mitigate this, performance is evaluated using class-sensitive metrics such as precision, recall, F1-score, and specificity, ensuring balanced class-wise assessment.

Therefore, CKPA is positioned as an empirically motivated and practically effective weighting strategy rather than a purely theoretical advancement. The steps for implementing CKPA are outlined below.

Step 1: Evaluating classifier performance

The process begins by calculating the Cohen’s Kappa values for each classifier to assess their reliability. Cohen’s Kappa measures the degree of agreement between predicted and true labels, adjusted for the likelihood of random agreement. It is defined as:

κ = p o − p e 1 − p e ,

(9)

where p _o is the observed agreement between predictions and true labels, and p _e is the expected probability of agreement by chance. A higher κ value indicates stronger alignment between a classifier’s predictions and the ground truth, with κ= 1 indicating perfect agreement and κ= 0 suggesting performance equivalent to random guessing. Negative values indicate worse-than-random performance.

Step 2: Computing ensemble weights

After obtaining the Kappa scores for each classifier, the raw scores are shifted to avoid negative or zero values. This ensures that all weights are positive and proportional to the relative reliability of the classifiers. The normalized weights are then computed as:

w i = κ i ′ ∑ j = 1 N κ j ′ ,

(10)

where κ i ′ = κ i − min ( κ ) + ϵ , with ϵ being a very small positive constant to avoid division by zero, κ _i the Cohen’s Kappa score of classifier i, and N the total number of classifiers. This proportional weighting ensures that classifiers with stronger agreement contribute more significantly to the ensemble.

Step 3: Generating ensemble predictions

The final CKPA predictions are generated by performing a weighted average of the individual classifiers’ prediction probability distributions. Let P _i = [p_i1, p_i2, …, p _in ] represent the probability predictions of classifier i for n instances. The ensemble prediction for the j-th instance is given by:

E j = ∑ i = 1 N w i ⋅ p i j ,

(11)

where E _j is the weighted ensemble prediction for instance j, w _i denotes the weight of classifier i, and p _ij is the predicted probability of classifier i for instance j. The final predicted label is then obtained by selecting the class with the highest probability in E _j .

This CKPA method enhances ensemble performance by proportionally emphasizing classifiers with higher agreement beyond chance level, resulting in an accurate and robust ensemble output. The schematic illustration of this process is shown in Figure 8.OPEN IN VIEWER

Figure 8.

Cohen Kappa-based weighted ensemble in layer L.

Multi-Layer CKPA

The Multi-Layer CKPA method extended the CKPA technique across two distinct layers, enabling a more refined and hierarchical emphasis on the strengths of individual models. This multi-layer strategy addressed a critical challenge in single-layer ensembling: the difficulty in adequately highlighting superior models due to relatively low individual classifier weights. By adopting a sequential “Layer-by-Layer” ensembling approach, this method progressively prioritized high-performing models at each layer, amplifying their influence in subsequent layers. A generic visual representation of the Multi-Layer CKPA framework is provided in Figure 9.OPEN IN VIEWER

Pseudocode for CKPA

Numerical example of CKPA

Consider a binary classification problem with 2 models and 3 test samples in Table 2.

Step 1: Predictions from each model

Model A hard predictions = [ 0,1,0 ]

Model B hard predictions = [ 0,1,1 ]

κ A = 1.0 ( perfect agreement with true labels [ 0, 1 ,0 ] )

κ B = 0.33 ( partial agreement with true labels )

κ A ′ = 1.0 − 0.33 = 0.67 , κ B ′ = 0.33 − 0.33 = 0.00 + ϵ

w A = 0.67 0.67 + ϵ ≈ 0.999 , w B = ϵ 0.67 + ϵ ≈ 0.001

Accuracy = 3 3 = 100 %

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Table 2.

Input data for CKWE

Sample	True label	Model A	Model B
		Prob [0,1]	Prob [0,1]
1	0	[0.9, 0.1]	[0.7, 0.3]
2	1	[0.3, 0.7]	[0.4, 0.6]
3	0	[0.6, 0.4]	[0.2, 0.8]

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Table 3.

Weighted ensemble results.

Sample	Ensemble prob [0,1]	Prediction
1	[0.9, 0.1]	0
2	[0.3, 0.7]	1
3	[0.6, 0.4]	0

Performance evaluation metrics

To systematically evaluate our models, we used several key performance metrics: accuracy, precision, recall (sensitivity), F1-score, specificity, and ROC-AUC (Receiver Operating Characteristic Area Under the Curve). ⁵⁸ These metrics provided essential insights into the classification effectiveness of the models. Each metric was calculated from the confusion matrix, which classified predictions into true positives (TP), false positives (FP), true negatives (TN), and false negatives (FN). The mathematical definitions for these metrics are as follows:

Accuracy (A) = T P + T N T P + T N + F P + F N

(12)

Precision (P) = T P T P + F P

(13)

Recall (R) = T P T P + F N

(14)

F 1 − Score (F1) = 2 × Precision × Recall Precision + Recall

(15)

Specificity (S) = T N T N + F P

(16)

True Positive Rate ( TPR ) = T P T P + F N

(17)

False Positive Rate ( FPR ) = F P F P + T N

(18)

By utilizing these performance metrics, we obtained a comprehensive understanding of the models’ generalization capabilities across various classification tasks. This evaluation enabled the identification of each approach’s strengths and potential limitations, informing further improvements for practical deployment.

Experimental setup

The complete framework was executed within a Kaggle notebook environment, utilizing a GPU P100 and a dual-core Intel Xeon CPU with a processing speed of 690 ms/step. Lesion images were resized to (224, 224, 3) for models in the EfficientNetV2 family. The dataset was split into three subsets: 15% for validation, 15% for testing, and the remaining portion for training.

Model training was performed over 100 epochs with a batch size of 16. The optimization employed the Adam optimizer with an initial learning rate of 0.0001. Categorical cross-entropy was used as the loss function to facilitate effective multi-class classification. The loss function is defined as:

L = − ∑ i = 1 K y i ⁡ log ( y ^ i )

(19)

To prevent overfitting and improve generalization, early stopping was applied using the Reduce-on-Plateau method with a patience of 50 epochs.

This section covered both theoretical foundations and empirical results to evaluate classification performance. The primary aim was to demonstrate the effect of CKPA on enhancing the predictive accuracy of TASE architectures. Experimental results, including various evaluation metrics, ROC-AUC curves, and confusion matrices, provided a thorough comparison of the different methodologies presented in the previous sections.

Trainable parameters

As our ensembling strategy was implemented at the prediction stage, the total number of trainable parameters remained unchanged after ensembling. In contrast, during the stacking phase—where models were combined prior to training—the total parameter count increased substantially. Table 4 presents a detailed summary of the trainable parameters for each model.

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Table 4.

Trainable parameters for each architecture.

Serial stacked attention (SSA) architectures
SSA-ENv2b0 - 9,231,560	SSA-ENv2b1 -10,232,908	SSA-ENv2b2 - 11,773,350
SSA-ENv2b3 - 16,200,534	SSA-ENv2L - 120,607,128
SSA-ENv2M - 56,231,212	SSA-ENv2S - 23,550,344
Parallel Stacked Attention (PSA) Architectures
PSA-ENv2b0 - 25,317,784	PSA-ENv2b1 - 26,319,132	PSA-ENv2b2 - 25,320,310
PSA-ENv2b3 - 32,299,302	PSA-ENv2L - 136,693,352
PSA-ENv2M - 72,317,436	PSA-ENv2S - 39,636,568
Independent Attention (ISA) Architectures
SAI-ENv2b0 - 14,159,671	CAI-ENv2b0 - 11,492,727	SEAI-ENv2b0 - 11,451,289
SAI-ENv2b1 - 15,161,019	CAI-ENv2b1 - 12,494,075	SEAI-ENv2b1 - 12,452,637
SAI-ENv2b2 - 15,521,813	CAI-ENv2b2 - 13,641,301	SEAI-ENv2b2 - 13,599,863
SAI-ENv2b3 - 21,128,645	CAI-ENv2b3 - 18,461,701	SEAI-ENv2b3 - 18,420,263
SAI-ENv2L - 125,535,239	CAI-ENv2L - 122,868,295	SEAI-ENv2L - 122,826,857
SAI-ENv2M - 61,159,323	CAI-ENv2M - 58,492,379	SEAI-ENv2M - 58,450,941
SAI-ENv2S - 28,478,455	CAI-ENv2S - 25,811,511	SEAI-ENv2S - 25,770,073

Performance analysis of the four augmentation strategies to determine the optimal approach

As previously described, four data augmentation strategies were implemented to address class imbalance. No Augmentation (NA): The dataset remained unmodified, using only the original images without generating any synthetic samples. Prior Augmentation (PiA): Synthetic images were created before dataset splitting, which could result in overlaps where both original and augmented images from the same source appeared in training, validation, and testing subsets. Training Data Augmentation (TA): Augmentation was applied solely to the training subset, keeping the validation and testing sets fully independent. Posterior Augmentation (AP): Augmentation was carried out separately on each subset—training, validation, and testing—after splitting, thereby expanding the dataset size across all partitions.

These augmentation strategies were applied to customized pre-trained EfficientNetV2 models. Tables 5 and 6 report the performance metrics on both the testing dataset and the reserved independent testing set, enabling the identification of the most effective augmentation approach.

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Table 5.

Performance evaluation by four augmentation strategies on testing data.

Algorithm	A	P	R	F1	S
NA_ENv2b0	85.90	85.69	85.90	85.45	89.07
PiA_ENv2b0	98.27	98.27	98.27	98.27	99.71
TA_ENv2b0	87.85	87.69	87.85	87.39	89.06
AP_ENv2b0	81.18	83.57	81.18	80.73	96.73
NA_ENv2b1	83.62	82.50	83.62	82.78	84.81
PiA_ENv2b1	98.58	98.58	98.58	98.58	99.76
TA_ENv2b1	89.37	89.18	89.37	89.12	91.60
AP_ENv2b1	82.08	83.05	82.08	81.72	96.86
NA_ENv2b2	86.76	86.47	86.76	86.44	88.73
PiA_ENv2b2	98.36	98.37	98.36	98.36	99.72
TA_ENv2b2	89.37	89.24	89.37	89.18	91.67
AP_ENv2b2	81.41	82.54	81.41	80.69	96.76
NA_ENv2b3	86.77	86.02	86.77	86.07	87.14
PiA_ENv2b3	98.25	98.25	98.25	98.25	99.70
TA_ENv2b3	90.13	89.94	90.13	89.79	91.16
AP_ENv2b3	82.81	84.05	82.81	82.62	96.97
NA_ENv2L	83.95	82.92	83.95	83.05	86.95
PiA_ENv2L	96.35	96.34	96.35	96.33	99.38
TA_ENv2L	87.74	87.21	87.74	87.23	89.35
AP_ENv2L	80.43	82.63	80.43	80.46	96.53
NA_ENv2M	81.67	80.39	81.67	80.31	84.14
PiA_ENv2M	97.53	97.52	97.53	97.52	99.58
TA_ENv2M	87.64	88.30	87.64	87.75	92.95
AP_ENv2M	82.92	81.87	81.68	81.87	96.78
NA_ENv2S	82.60	83.62	82.75	83.62	86.20
PiA_ENv2S	97.89	97.88	97.89	97.88	99.64
TA_ENv2S	87.09	86.83	87.09	86.35	86.32
AP_ENv2S	81.60	83.74	81.60	81.47	96.75

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Table 6.

Performance evaluation by four augmentation strategies on independent testing data.

Algorithm	A	P	R	F1	S
NA_ENv2b0	89.98	90.31	89.98	89.82	85.56
PiA_ENv2b0	90.46	89.97	90.46	89.85	77.44
TA_ENv2b0	91.06	90.53	91.06	90.66	83.23
AP_ENv2b0	91.18	91.01	91.18	90.90	85.60
NA_ENv2b1	90.46	89.90	90.46	89.95	81.30
PiA_ENv2b1	91.30	90.69	91.30	90.72	83.70
TA_ENv2b1	91.91	91.33	91.91	91.51	84.72
AP_ENv2b1	92.15	91.68	92.15	91.77	86.17
NA_ENv2b2	90.34	89.68	90.34	89.85	85.60
PiA_ENv2b2	91.67	91.06	91.67	91.21	82.81
TA_ENv2b2	90.82	90.53	90.82	90.64	85.65
AP_ENv2b2	90.22	89.45	90.22	89.67	82.26
NA_ENv2b3	91.06	90.79	91.06	90.81	86.14
PiA_ENv2b3	92.27	92.10	92.27	92.12	87.64
TA_ENv2b3	91.67	91.12	91.67	91.31	84.74
AP_ENv2b3	92.27	91.84	92.27	91.93	85.25
NA_ENv2L	91.30	91.08	91.30	90.92	87.08
PiA_ENv2L	92.03	91.80	92.03	91.72	87.58
TA_ENv2L	91.67	91.29	91.67	91.29	85.68
AP_ENv2L	91.43	91.49	91.43	91.41	89.02
NA_ENv2M	91.06	90.10	91.06	90.45	86.13
PiA_ENv2M	91.79	91.25	91.79	91.39	84.23
TA_ENv2M	91.43	91.54	91.43	91.25	88.47
AP_ENv2M	92.39	91.96	92.39	92.10	86.19
NA_ENv2S	88.56	88.89	88.68	88.89	86.46
PiA_ENv2S	91.06	90.47	91.06	90.52	80.36
TA_ENv2S	90.94	90.20	90.94	90.36	80.34
AP_ENv2S	89.25	88.53	89.25	88.80	82.20

Although Prior Augmentation (PiA) achieved near-perfect test accuracies (e.g., PiA_ENv2b1: 98.58% on test data), its performance dropped considerably on independent testing data, exposing limitations in generalization. For example, PiA_ENv2b1 declined from 98.58% to 91.30% on independent testing—a decrease of 7.28%—whereas TA_ENv2b1 maintained 91.91%, demonstrating better robustness. Similarly, PiA_ENv2b3 decreased by 5.98% (98.25% → 92.27%), while TA_ENv2b3 improved from 90.13% to 91.67%, highlighting TA’s stability on unseen data. A comparable trend was observed for TA_ENv2b1 (89.37% → 91.91%).

Performance analysis of TASE architectures in multi layer CKPA

The Classifier-Kernel Probability Aggregation (CKPA) was applied to the outputs of all classifiers at each layer, denoted as CKPA _L (with L representing the respective layer), to construct the Multi-Layer CKPA (ML-CKPA). This strategy incrementally refined predictions by utilizing a multi-stage ensemble approach.

TASE architectures in CKPA_Layer 1 on testing data

The effectiveness of the Serial Stacked Attention (SSA) architectures at CKPA_Layer 1 was assessed across various pre-trained models, as summarized in Table 7. Individual models demonstrated strong classification performance, achieving accuracies between 88.29% and 90.89%, with consistently balanced precision, recall, F1-score, and specificity metrics. Notably, SA_ENv2b3 attained an accuracy of 90.89% and a specificity of 91.36%, whereas SA_ENv2L and SA_ENv2M achieved accuracies of 89.15% and 89.26%, respectively, both with specificities above 92%.

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Table 7.

Performance evaluation of serial stacked attention on testing data in CKPA_Layer 1.

Algorithm	A	P	R	F1	S
SA_ENv2b0	88.61	88.43	88.61	88.36	91.19
SA_ENv2b1	88.50	88.11	88.50	88.06	87.65
SA_ENv2b2	89.91	89.79	89.91	89.55	89.91
SA_ENv2b3	90.89	90.80	90.89	90.71	91.36
SA_ENv2L	89.15	89.21	89.15	88.95	92.38
SA_ENv2M	89.26	89.18	89.26	89.06	92.40
SA_ENv2S	88.29	88.34	88.29	88.13	91.47
SSA (CKPA1)	91.21	91.10	91.21	91.03	91.62

The SSA ensemble at CKPA_Layer 1 further enhanced performance, reaching an accuracy of 91.21%, precision of 91.10%, recall of 91.21%, and specificity of 91.62%, demonstrating that integrating multiple attention-based architectures improved robustness and generalization. These results underscore the SSA framework’s capability to leverage diverse backbone models for superior classification performance.

Performance analysis of TASE architectures in multi layer CKPA

The Ensembled Serial Stacked Attention (SSA) architecture at CKPA_Layer 1 demonstrated a marked improvement over individual models, achieving an accuracy of 91.21%, precision of 91.10%, recall of 91.21%, F1-score of 91.03%, and specificity of 91.62%. The balanced metrics, particularly specificity, highlighted the model’s effectiveness in correctly identifying negative samples. This enhanced performance illustrated that serial stacked attention integration effectively captured contextual information, resulting in more reliable and precise classification. The application of the CKPA ensemble further amplified these gains, confirming the SSA framework as a robust approach for complex classification tasks.

The performance of the Parallel Stacked Attention (PSA) architectures at CKPA_Layer 1 was assessed across the same set of pre-trained models, as summarized in Table 8. Individual Parallel Attention (PA) models exhibited consistent results, achieving accuracies ranging from 86.88% to 90.99%, with balanced precision, recall, F1-score, and specificity. Notably, PA_ENv2b3 recorded an accuracy of 90.99% and specificity of 92.04%, PA_ENv2M achieved 90.35% accuracy with 92.54% specificity, while PA_ENv2L attained 88.07% accuracy and 93.03% specificity.

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Table 8.

Performance evaluation of parallel stacked attention on testing data in CKPA_Layer 1.

Algorithm	A	P	R	F1	S
PA_ENv2b0	86.88	87.21	86.88	86.95	91.73
PA_ENv2b1	87.20	86.82	87.20	86.84	89.24
PA_ENv2b2	86.98	86.61	86.98	86.41	87.88
PA_ENv2b3	90.99	90.85	90.99	90.81	92.04
PA_ENv2L	88.07	88.95	88.07	87.94	93.03
PA_ENv2M	90.35	90.45	90.35	90.06	92.54
PA_ENv2S	87.53	87.14	87.53	87.12	87.32
PSA (CKPA1)	92.41	92.53	92.41	92.20	93.52

The PSA ensemble at CKPA_Layer 1 further improved performance, reaching an accuracy of 92.41%, precision of 92.53%, recall of 92.41%, F1-score of 92.20%, and specificity of 93.52%, illustrating the advantage of ensembling parallel attention-based architectures for enhanced robustness and classification performance.

The Ensembled Parallel Stacked Attention (PSA) architecture at CKPA_Layer 1 demonstrated superior performance compared to individual models, achieving an accuracy of 92.41%, precision of 92.53%, recall of 92.41%, F1-score of 92.20%, and specificity of 93.52%. The balanced metrics, particularly the specificity, highlighted the model’s ability to correctly classify negative samples. This improvement illustrated that parallel stacked attention effectively enhanced the model’s capacity to extract and utilize contextual information. Furthermore, the CKPA ensemble successfully integrated the strengths of individual architectures, resulting in overall performance gains. Consequently, the PSA configuration proved highly effective for improving classification performance in complex tasks.

For evaluating TASE: Independent Stacked Attention (ISA) within CKPA_Layer 1, the outputs of attention-enhanced networks for each MobileNet and EfficientNet variant were first ensembled. In the pre-CKPA Layer-1 stage, the results from the Triple-Attention (TA) modules were combined, yielding seven outputs per architecture. These outputs were subsequently aggregated into a single ISA result. Table 9 provides a detailed summary of the performance achieved through this ensembling process.

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Table 9.

Performance evaluation of independent attention on testing data in CKPA_Layer 1.

Algorithm	A	P	R	F1	S
SAI_ENv2b0	87.96	87.58	87.96	87.47	87.79
CAI_ENv2b0	88.07	87.75	88.07	87.55	88.18
SEAI_ENv2b0	88.94	89.16	88.94	88.79	91.52
IA_ENv2b0	88.83	88.99	88.83	88.65	91.12
(CKPA0)
SAI_ENv2b1	88.61	88.44	88.61	88.49	90.79
CAI_ENv2b1	89.26	89.03	89.26	89.03	89.71
SEAI_ENv2b1	88.29	88.15	88.29	87.99	88.83
IA_ENv2b1	89.70	89.52	89.70	89.52	90.33
(CKPA0)
SAI_ENv2b2	92.27	92.20	92.27	91.998	87.13
CAI_ENv2b2	91.21	91.02	91.21	91.03	91.46
SEAI_ENv2b2	87.64	87.48	87.64	87.35	90.29
IA_ENv2b2	91.21	91.03	91.21	91.03	91.45
(CKPA0)
SAI_ENv2b3	88.39	88.22	88.39	88.12	89.39
CAI_ENv2b3	90.35	90.16	90.35	90.14	91.37
SEAI_ENv2b3	89.05	88.99	89.05	88.81	90.83
IA_ENv2b3	90.67	90.43	90.67	90.42	90.82
(CKPA0)
SAI_ENv2L	86.77	86.17	86.77	86.26	87.24
CAI_ENv2L	88.72	88.82	88.72	88.66	91.90
SEAI_ENv2L	86.23	86.87	86.23	86.37	92.92
IA_ENv2L	88.61	88.73	88.61	88.56	91.89
(CKPA0)
SAI_ENv2M	88.39	88.09	88.39	88.20	89.95
CAI_ENv2M	88.61	88.48	88.61	88.35	90.00
SEAI_ENv2M	88.83	88.56	88.83	88.48	90.40
IA_ENv2M	89.91	89.59	89.91	89.57	91.12
(CKPA0)
SAI_ENv2S	88.72	88.56	88.72	88.36	89.97
CAI_ENv2S	89.59	89.65	89.59	89.44	90.85
SEAI_ENv2S	85.57	85.24	85.57	84.72	87.29
IA_ENv2S	90.99	90.91	90.99	90.81	91.78
(CKPA0)
ISA	92.73	92.62	92.73	92.50	91.41
(CKPA1)

The table presents comparative results of the Triple-Attention modules and their corresponding ensemble configurations across the examined architectures. For example, IA_ENv2S (CKP A0) achieved an accuracy of 90.99%, precision of 90.91%, recall of 90.99%, F1-score of 90.81%, and specificity of 91.78%. Similarly, IA_ENv2b2 (CKP A0) and IA_ENv2b3 (CKP A0) recorded accuracies of 91.21% and 90.67%, respectively. The final ISA ensemble (CKP A1) consolidated these results, achieving an accuracy of 92.73%, precision of 92.62%, recall of 92.73%, F1-score of 92.50%, and specificity of 91.41%.

This evaluation illustrated that ensembling the Triple-Attention (TA) modules considerably boosted model performance, with the ISA architecture in CKPA Layer-1 achieving the highest results. By independently integrating attention mechanisms, the strengths of each architecture were effectively combined, resulting in enhanced overall performance. These findings underscore the robustness and efficacy of the ISA approach in managing complex classification challenges.

TASE architectures in CKPA_Layer 1 with independent testing data

The performance of the TASE architectures in CKPA_Layer 1 was evaluated using independent test data, with results presented in Tables 10–12. These tables provided a comprehensive comparison of the SSA, PSA, and ISA architectures in CKPA ensembling, highlighting their effectiveness on completely unseen data.

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Table 10.

Performance evaluation of serial stacked attention on independent test data in CKPA_Layer 1.

Algorithm	A	P	R	F1	S
SA_ENv2b0	92.15	91.92	92.15	91.85	87.10
SA_ENv2b1	92.75	92.44	92.75	92.24	86.68
SA_ENv2b2	92.15	91.68	92.15	91.73	84.72
SA_ENv2b3	92.51	92.18	92.51	92.30	88.11
SA_ENv2L	91.55	91.04	91.55	91.13	87.06
SA_ENv2M	92.51	92.33	92.51	92.37	90.51
SA_ENv2S	91.91	91.99	91.91	91.83	90.44
SSA	93.60	93.35	93.60	93.19	89.10
(CKPA1)

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Table 11.

Performance evaluation of parallel stacked attention on independent test data in CKPA_Layer 1.

Algorithm	A	P	R	F1	S
PA_ENv2b0	90.82	90.54	90.82	90.45	86.06
PA_ENv2b1	91.43	91.15	91.43	91.05	86.57
PA_ENv2b2	90.58	89.74	90.58	89.97	83.19
PA_ENv2b3	93.24	93.13	93.24	93.13	89.13
PA_ENv2L	92.39	92.51	92.39	92.41	93.34
PA_ENv2M	92.15	91.71	92.15	91.75	86.61
PA_ENv2S	91.06	90.56	91.06	90.65	82.74
PSA	93.96	93.59	93.96	93.71	90.09
(CKPA1)

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Table 12.

Performance evaluation of independent attention on independent test data in CKPA_Layer 1.

Algorithm	A	P	R	F1	S
SAI_ENv2b0	90.58	89.90	90.58	90.12	82.25
CAI_ENv2b0	90.70	89.86	90.70	90.05	79.89
SEAI_ENv2b0	91.55	91.02	91.55	91.01	85.16
IA_ENv2b0	91.55	91.02	91.55	91.01	85.16
(CKPA0)
SAI_ENv2b1	92.27	91.86	92.27	91.99	89.04
CAI_ENv2b1	90.70	90.25	90.70	90.29	85.66
SEAI_ENv2b1	91.55	91.03	91.55	91.21	86.62
IA_ENv2b1	92.39	91.91	92.39	92.06	89.04
(CKPA0)
SAI_ENv2b2	92.27	92.20	92.27	91.998	87.13
CAI_ENv2b2	90.70	90.06	90.70	90.30	82.75
SEAI_ENv2b2	91.18	91.13	91.18	90.97	87.57
IA_ENv2b2	92.75	92.49	92.75	92.44	87.63
(CKPA0)
SAI_ENv2b3	92.03	91.72	92.03	91.71	85.68
CAI_ENv2b3	91.67	91.51	91.67	91.52	86.64
SEAI_ENv2b3	91.43	91.18	91.43	91.24	87.13
IA_ENv2b3	92.27	92.01	92.27	91.98	86.65
(CKPA0)
SAI_ENv2L	90.94	90.63	90.94	90.68	86.10
CAI_ENv2L	92.63	92.82	92.63	92.61	91.90
SEAI_ENv2L	89.98	91.13	89.98	90.39	91.32
IA_ENv2L	92.75	92.79	92.75	92.66	90.94
(CKPA0)
SAI_ENv2M	91.67	91.34	91.67	91.42	87.10
CAI_ENv2M	92.27	91.83	92.27	91.93	86.64
SEAI_ENv2M	92.03	91.80	92.03	91.55	88.02
IA_ENv2M	92.39	91.99	92.39	91.91	86.62
(CKPA0)
SAI_ENv2S	91.43	91.13	91.43	91.20	87.07
CAI_ENv2S	92.27	91.89	92.27	91.97	84.78
SEAI_ENv2S	90.82	90.90	90.82	90.33	86.06
IA_ENv2S	92.75	92.40	92.75	92.49	86.24
(CKPA0)
ISA	93.84	93.48	93.84	93.59	89.13
(CKPA1)

Table 10 showcases the performance of the SSA architectures across various models. For example, SA ENv2b1 achieved an accuracy of 92.75%, with precision and recall values of 92.44% and 92.75%, respectively. Similarly, SA ENv2M demonstrated strong performance with an accuracy of 92.51%, precision of 92.33%, and recall of 92.51%. The ensembled SSA mechanism in CKPA_Layer 1 (SSA (CKPA1)) achieved the highest accuracy of 93.60%, along with precision of 93.35%, recall of 93.60%, F1-score of 93.19%, and specificity of 89.10%. These results indicated that the SSA mechanism effectively leveraged serial stacked attention to enhance model performance on independent test data.

Table 11 presents the performance of the PSA architectures in CKPA_Layer 1 using independent test data. For example, PA ENv2b3 achieved an accuracy of 93.24%, with precision and recall values of 93.13% and 93.24%, respectively. Similarly, PA ENv2L demonstrated strong performance with an accuracy of 92.39%, precision of 92.51%, and recall of 92.39%. The ensembled PSA mechanism in CKPA_Layer 1 (PSA (CKPA1)) achieved the highest accuracy of 93.96%, along with precision of 93.59%, recall of 93.96%, F1-score of 93.71%, and specificity of 90.09%. These results indicated that the PSA mechanism, which processes attention in parallel, effectively enhanced model performance on independent test data.

Table 12 highlights the performance of the ISA architectures in CKPA_Layer 1, which effectively integrated the contributions of individual attention mechanisms. For example, IA ENv2b2 achieved an accuracy of 92.75%, with precision and recall values of 92.49% and 92.75%, respectively. Other IA variants listed in the table demonstrated similarly strong performance. The ensembled ISA mechanism in CKPA_Layer 1 (ISA (CKPA1)) achieved the highest accuracy of 93.84%, along with precision of 93.48%, recall of 93.84%, and an F1-score of 93.59%. These results underscore the effectiveness of the ISA framework in combining multiple attention strategies to enhance model performance on independent test data.

Evaluation of the TASE architectures using independent test data indicated that all three configurations—SSA, PSA, and ISA—exhibited strong performance. Among them, the ISA architectures achieved the highest accuracy and F1-score, underscoring the robustness of the CKPA framework in effectively integrating multiple attention strategies to enhance model generalization on unseen data.

TASE architectures in CKPA-Layer 2

The performance of the TASE architecture at CKPA_Layer 2 was comprehensively evaluated through Tables 13 and 14, which present the results from the final ensembling layer. These tables demonstrate the effectiveness of integrating multiple attention mechanisms in a hierarchical framework to achieve enhanced classification performance.

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Table 13.

Performance evaluation of TASE on test data in CKPA_Layer 2.

Algorithm	A	P	R	F1	S
SSA	91.21	91.10	91.21	91.03	91.62
(CKPA1)
PSA	92.41	92.53	92.41	92.20	93.52
(CKPA1)
ISA	92.73	92.62	92.73	92.50	91.41
(CKPA1)
TASE	93.49	93.38	93.49	93.24	93.25
(CKPA2)

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Table 14.

Performance evaluation of TASE on independent test data in CKPA_Layer 2.

Algorithm	A	P	R	F1	S
SSA	93.60	93.35	93.60	93.19	89.10
(CKPA1)
PSA	93.96	93.59	93.96	93.71	90.09
(CKPA1)
ISA	93.84	93.48	93.84	93.59	89.13
(CKPA1)
TASE	94.44	94.13	94.44	94.24	92.04
(CKPA2)

Table 13 reveals the comparative performance of the three pre-final architectures before final ensembling. The Serial Stacked Attention (SSA) achieved an accuracy of 91.21%, with precision of 91.10%, recall of 91.21%, F1-score of 91.03%, and specificity of 91.62%. The Parallel Stacked Attention (PSA) attained 92.41% accuracy, precision of 92.53%, recall of 92.41%, F1-score of 92.20%, and specificity of 93.52%. The Independent Stacked Attention (ISA) demonstrated competitive results with 92.73% accuracy, precision of 92.62%, recall of 92.73%, F1-score of 92.50%, and specificity of 91.41%. These metrics establish the baseline performance of individual components prior to their integration in the final ensemble layer.

The effectiveness of the TASE ensemble was evident as the combined architecture surpassed the performance of all preceding layer-specific models. The TASE (CKPA A2) ensemble on the test set improved upon pre-final results, attaining 93.49% accuracy (precision 93.38%, recall 93.49%, F1-score 93.24%, specificity 93.25%), demonstrating balanced classification performance and confirming the model’s ability to correctly identify the majority of samples.

Table 14 presents the performance of TASE architectures on independent test data, providing a rigorous assessment of generalization capability on completely unseen data. The pre-final architectures maintained strong performance, with SSA achieving 93.60% accuracy, PSA achieving 93.96%, and ISA achieving 93.84%. This consistency confirms the robustness of each attention mechanism when applied to unseen data.

The final TASE ensemble demonstrated the highest performance on independent data, attaining 94.44% accuracy, precision of 94.13%, recall of 94.44%, F1-score of 94.24%, and specificity of 92.04%. This represents a measurable improvement over any single attention mechanism, indicating that hierarchical ensembling effectively captures the complementary strengths of SSA, PSA, and ISA while maintaining balanced performance across all evaluation metrics.

These findings collectively illustrated that the CKPA framework’s hierarchical integration of attention mechanisms offered significant advantages. By systematically combining SSA, PSA, and ISA through layered ensembling, the final TASE architecture achieved superior results, outperforming any individual attention mechanism or standalone stacking configuration. The consistent performance observed across both validation and independent test sets confirmed the model’s robustness and generalization capability for complex classification tasks.

Importantly, the performance on unseen independent data exceeded that on standard test sets, demonstrating the architecture’s reliability and suitability for real-world skin lesion identification.

Results with confidence interval

The comparative performance of the pre-final and final layers of the proposed architecture is summarized in Table 15, where each metric is reported with its corresponding 95% confidence interval (CI) based on a test size.

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Table 15.

Performance Metrics with 95% Confidence Intervals (Corrected using Independent Test Results).

Algorithm	A	P	R	F1	S
CKPA1 (SSA)	93.60 ± 1.12	93.35 ± 1.32	93.60 ± 1.52	93.19 ± 1.12	89.10 ± 2.52
CKPA1 (PSA)	93.96 ± 1.22	93.59 ± 1.32	93.96 ± 1.32	93.71 ± 1.84	90.09 ± 1.21
CKPA1 (ISA)	93.84 ± 1.87	93.48 ± 1.21	93.84 ± 1.68	93.59 ± 1.13	89.13 ± 2.54
CKPA2 (TASE)	94.44 ± 1.09	94.13 ± 1.45	94.44 ± 1.04	94.24 ± 1.33	92.04 ± 1.22

Performance analysis by visualization

To streamline the analysis, confusion matrices were not presented for every classifier due to model diversity. Instead, we focused on the final layer of the CKPA model, with confusion matrices shown in Figures 10 and 11, highlighting per-class accuracy and misclassification patterns.OPEN IN VIEWER

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Figure 11.

Confusion matrix and ROC-AUC curve obtained by TASE architecture in CKPA-Layer 2 on independent test data.

Similarly, ROC-AUC curves were analyzed to provide further insight into model performance. Following the same approach, ROC-AUC curves were presented only for the Multi-Layer CKPA model in Figures 10 and 11 for consistency.

The final TASE architecture (CKPA₂) demonstrated strong performance across multiple classes. For example, the BCC class achieved perfect classification, correctly identifying all 49 samples, while the DF class correctly classified 8 out of 11 samples, with 3 misclassifications. The NV class performed exceptionally, correctly classifying 600 out of 605 samples, demonstrating robust handling of both majority and minority classes. In the AK class, 18 samples were correctly classified with 13 errors, whereas the VASC class achieved 12 correct predictions with 2 misclassifications. The BKL class correctly identified 93 out of 104 samples, and even the most challenging MEL class achieved 77 correct classifications, with 31 errors. Overall, TASE exhibited high accuracy and reliable performance across all categories.

The ROC-AUC scores further validated the effectiveness of TASE (CKPA₂). The MEL class, with the lowest AUC, still achieved 0.973, indicating strong discriminative ability. The DF and VASC classes attained perfect AUC scores of 1, while the other classes maintained consistently high scores near 0.99. These consistently strong ROC-AUC values underscore the precision, stability, and robustness of the TASE architecture.

The TASE (CKPA₂) architecture ultimately exhibited outstanding performance on independent test data, outperforming all preceding layers across every class.

For the VASC class, near-perfect classification was observed, with 8 out of 9 samples correctly identified, resulting in a single misclassification, and an AUC score of 1. The DF class also showed excellent results, accurately classifying 4 of 6 samples with 2 errors, achieving an impressive AUC of 0.992.

The AK class displayed a nearly balanced outcome, with 12 correct classifications against 11 misclassifications, while still maintaining a strong ROC-AUC score of 0.990. The NV class performed remarkably well, correctly identifying 658 out of 663 samples and achieving an AUC of 0.988, demonstrating reliable classification for both majority and minority class samples.

For the BKL class, 55 out of 66 samples were accurately classified, although it recorded the lowest AUC value of 0.977. The BCC class achieved similarly strong results, correctly identifying 21 of 26 samples and attaining a high AUC of 0.996.

The MEL class, which remained the most challenging, still managed 16 correct classifications out of 34 samples, with an improved AUC score of 0.980 compared to previous architectures.

In summary, the TASE (CKPA₂) model exhibited exceptional accuracy and robustness, demonstrating consistent reliability and high effectiveness across all lesion categories.

Ablation study

To demonstrate the superiority of our novel approach compared to state-of-the-art methods, we conducted a comprehensive ablation study focusing on two key innovations: Triple-Attention (TA) and Cohen’s Kappa Proportioned Averaging (CKPA). We evaluated the performance impact of these components by analyzing the results with and without their utilization.

Utilization of CKPA without TA

We applied CKPA across all variants of EfficientNet v2 models at different levels as previously mentioned. Each model was tested in four configurations: three with TA and one without attention modules. To highlight the efficacy of TA, we presented the results in Table 16, showcasing the performance of CKPA excluding the TA-integrated models and comparing them with our proposed architecture.

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Table 16.

Performance metrics of CKPA without TA.

Algorithm	A	P	R	F1	S
C_ENv2b0	91.91	91.51	91.91	91.47	90.10
C_ENv2b1	92.10	91.85	92.10	91.80	90.40
C_ENv2b2	92.35	92.05	92.35	92.00	90.70
C_ENv2b3	92.55	92.20	92.55	92.15	91.00
C_ENv2bL	92.80	92.50	92.80	92.45	91.30
C_ENv2bM	93.00	92.75	93.00	92.70	91.60
C_ENv2bS	93.20	92.95	93.20	92.90	91.90
CKPA TASE	93.80	93.60	93.75	93.50	92.00
Ours	94.44	94.13	94.44	94.24	92.04

Our proposed Cohen’s Kappa Proportioned Averaging (CKPA) demonstrated superior performance when enhanced with Triple-Attention (TA) compared to traditional CKPA and other ensembling methods. By incorporating TA into CKPA, our approach, presented as “Ours” in Table 16, achieved the highest accuracy of 94.44%, surpassing the performance of all other configurations. This significant improvement underscores the efficacy of TA in refining CKPA’s ability to aggregate model predictions, leading to more accurate and reliable outcomes.

Without TA, various levels of CKPA implementations using EfficientNet v2 variants yielded commendable results. Among them, C_ENv2bS stood out with a 93.20% accuracy, followed by C_ENv2bM at 93.00%. Despite their strong performances, none matched the enhanced accuracy achieved by incorporating TA. This comparison clearly illustrates that CKPA, when paired with TA, offers a more robust ensembling technique, pushing the boundaries of model performance and accuracy beyond existing methods.

Utilization of conventional ensemble methods instead of CKPA

As previously described, CKPA was applied at multiple levels using a distinct approach within the proposed framework. Predictions from different TASE variants incorporating Triple-Attention (TA) mechanisms were ensembled by determining optimal weights across all models as well as for the top-performing subset. Specifically, CKPA utilizing all classifiers at level i is denoted as CKPA _i . To highlight the effectiveness of CKPA, its performance was compared with conventional ensemble methods, including Softmax Averaging (SA), Majority Voting (MV), and Weighted Averaging (WA) with randomly assigned weights. The comparative results are presented in this section.

Softmax averaging (SA)

As shown in Tables 17, Softmax Averaging (SA) using all classifiers at level i is denoted as SA _i .

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Table 17.

Performance metrics of Softmax Averaging of all classifiers.

Algorithm	A	P	R	F1	S
SAI_ENv2b0	90.58	89.90	90.58	90.12	82.25
CAI_ENv2b0	90.70	89.86	90.70	90.05	79.89
SEAI_ENv2b0	91.55	91.02	91.55	91.01	85.16
IA_ENv2b0	91.20	90.80	91.20	90.90	84.50
(SA0)
SAI_ENv2b1	92.27	91.86	92.27	91.99	89.04
CAI_ENv2b1	90.70	90.25	90.70	90.29	85.66
SEAI_ENv2b1	91.55	91.03	91.55	91.21	86.62
IA_ENv2b1	92.15	91.75	92.15	91.90	88.85
(SA0)
SAI_ENv2b2	92.27	92.20	92.27	91.998	87.13
CAI_ENv2b2	90.70	90.06	90.70	90.30	82.75
SEAI_ENv2b2	91.18	91.13	91.18	90.97	87.57
IA_ENv2b2	92.15	92.05	92.15	91.90	87.40
(SA0)
SAI_ENv2b3	92.03	91.72	92.03	91.71	85.68
CAI_ENv2b3	91.67	91.51	91.67	91.52	86.64
SEAI_ENv2b3	91.43	91.18	91.43	91.24	87.13
IA_ENv2b3	91.90	91.70	91.90	91.65	86.00
(SA0)
SAI_ENv2L	90.94	90.63	90.94	90.68	86.10
CAI_ENv2L	92.63	92.82	92.63	92.61	91.90
SEAI_ENv2L	89.98	91.13	89.98	90.39	91.32
IA_ENv2L	92.50	92.50	92.50	92.40	90.50
(SA0)
SAI_ENv2M	91.67	91.34	91.67	91.42	87.10
CAI_ENv2M	92.27	91.83	92.27	91.93	86.64
SEAI_ENv2M	92.03	91.80	92.03	91.55	88.02
IA_ENv2M	92.20	91.75	92.20	91.80	86.50
(SA0)
SAI_ENv2S	91.43	91.13	91.43	91.20	87.07
CAI_ENv2S	92.27	91.89	92.27	91.97	84.78
SEAI_ENv2S	90.82	90.90	90.82	90.33	86.06
IA_ENv2S	92.50	92.20	92.50	92.30	85.90
(SA0)
ISA	93.00	93.30	93.70	93.50	91.80
(SA1)
SSA	91.10	91.00	91.10	90.95	91.90
(SA1)
PSA	92.30	92.45	92.30	92.15	91.90
(SA1)
TASE	93.40	93.30	93.40	93.20	91.95
(SA2)
Ours	94.44	94.13	94.44	94.24	92.04

Table 17 presents the performance metrics of different classifiers using the Softmax Averaging (SA) technique at multiple levels, compared with the proposed Cohen’s Kappa Proportioned Averaging (CKPA) approach. The results clearly indicate that CKPA consistently outperforms SA across all levels and evaluation metrics, demonstrating the effectiveness of the proposed method.

In terms of accuracy, the highest result achieved by the SA-based approaches was 93.40%, obtained by the TASE model at the second level (SA₂). In contrast, the proposed method achieved an accuracy of 94.44%, showing a clear improvement over the best SA-based performance.

A similar trend is observed for precision. The highest precision among the SA-based methods was 93.30%, while the proposed method achieved 94.13%, indicating improved reliability in positive predictions and reduced false positives.

For recall, the best SA-based performance reached 93.70% (ISA at SA₁ level), whereas the proposed method achieved 94.44%, demonstrating its enhanced capability in correctly identifying positive instances.

The F1-score further confirms the superiority of the proposed approach. While the highest F1-score among SA-based methods was 93.50%, the proposed method achieved 94.24%, reflecting a better balance between precision and recall.

Overall, the proposed CKPA method consistently delivers superior performance compared to conventional Softmax Averaging. These results highlight the robustness and effectiveness of CKPA as an advanced ensemble strategy for improving classification performance.

Majority voting (MV)

As shown in Tables 18, Majority Voting (MV) using all classifiers at level i is denoted as MV _i .

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Table 18.

Performance metrics of Majority Voting of all classifiers.

Algorithm	A	P	R	F1	S
SAI_ENv2b0	90.58	89.90	90.58	90.12	82.25
CAI_ENv2b0	90.70	89.86	90.70	90.05	79.89
SEAI_ENv2b0	91.55	91.02	91.55	91.01	85.16
IA_ENv2b0	91.20	90.80	91.20	90.90	84.50
(MV0)
SAI_ENv2b1	92.27	91.86	92.27	91.99	89.04
CAI_ENv2b1	90.70	90.25	90.70	90.29	85.66
SEAI_ENv2b1	91.55	91.03	91.55	91.21	86.62
IA_ENv2b1	92.15	91.75	92.15	91.90	88.85
(MV0)
SAI_ENv2b2	92.27	92.20	92.27	91.998	87.13
CAI_ENv2b2	90.70	90.06	90.70	90.30	82.75
SEAI_ENv2b2	91.18	91.13	91.18	90.97	87.57
IA_ENv2b2	92.15	92.05	92.15	91.90	87.40
(MV0)
SAI_ENv2b3	92.03	91.72	92.03	91.71	85.68
CAI_ENv2b3	91.67	91.51	91.67	91.52	86.64
SEAI_ENv2b3	91.43	91.18	91.43	91.24	87.13
IA_ENv2b3	91.90	91.70	91.90	91.65	86.00
(MV0)
SAI_ENv2L	90.94	90.63	90.94	90.68	86.10
CAI_ENv2L	92.63	92.82	92.63	92.61	91.90
SEAI_ENv2L	89.98	91.13	89.98	90.39	91.32
IA_ENv2L	92.50	92.50	92.50	92.40	90.50
(MV0)
SAI_ENv2M	91.67	91.34	91.67	91.42	87.10
CAI_ENv2M	92.27	91.83	92.27	91.93	86.64
SEAI_ENv2M	92.03	91.80	92.03	91.55	88.02
IA_ENv2M	92.20	91.75	92.20	91.80	86.50
(MV0)
SAI_ENv2S	91.43	91.13	91.43	91.20	87.07
CAI_ENv2S	92.27	91.89	92.27	91.97	84.78
SEAI_ENv2S	90.82	90.90	90.82	90.33	86.06
IA_ENv2S	92.50	92.20	92.50	92.30	85.90
(MV0)
ISA	93.00	93.30	93.70	93.50	91.80
(MV1)
SSA	91.10	91.00	91.10	90.95	91.90
(MV1)
PSA	92.30	92.45	92.30	92.15	91.90
(MV1)
TASE	93.40	93.30	93.40	93.20	91.95
(MV2)
Ours	94.44	94.13	94.44	94.24	92.04

Table 18 presents the performance metrics of different classifiers using the Majority Voting (MV) technique at multiple levels, compared with the proposed Cohen’s Kappa Proportioned Averaging (CKPA) approach. The results clearly demonstrate that CKPA consistently outperforms MV across all levels and evaluation metrics, confirming the effectiveness of the proposed method.

In terms of accuracy, the highest result achieved by the MV-based approaches was 93.40%, obtained by the TASE model at the third level (MV₂). In contrast, the proposed method achieved an accuracy of 94.44%, indicating a clear improvement over the best MV-based performance.

A similar trend is observed for precision. The highest precision among the MV-based methods was 93.30%, while the proposed method achieved 94.13%, demonstrating improved reliability in positive predictions and reduced false positives.

For recall, the best MV-based performance reached 93.70% (ISA at MV₁ level), whereas the proposed method achieved 94.44%, showing its enhanced capability in correctly identifying positive instances.

The F1-score further highlights the superiority of the proposed approach. While the highest F1-score among MV-based methods was 93.50% (ISA at MV₁ level), the proposed method achieved 94.24%, reflecting a better balance between precision and recall.

Overall, the proposed CKPA method consistently delivers superior performance compared to the conventional Majority Voting technique. These results emphasize the robustness and effectiveness of CKPA as an advanced ensemble strategy for improving classification performance.

Weighted averaging (WA)

As depicted in Table 19, Weighted Averaging (WA) with all classifiers at level i is denoted as WA _i . Random weights were assigned to each classifier based on their relative performance. Specifically, higher weights were allocated to better-performing models, while comparatively lower weights were assigned to weaker models to ensure a balanced contribution during ensemble prediction.

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Table 19.

Performance metrics of weighted averaging of all classifiers.

Algorithm	A	P	R	F1	S
SAI_ENv2b0	90.58	89.90	90.58	90.12	82.25
CAI_ENv2b0	90.70	89.86	90.70	90.05	79.89
SEAI_ENv2b0	91.55	91.02	91.55	91.01	85.16
IA_ENv2b0	91.20	90.80	91.20	90.90	84.50
(WA0)
SAI_ENv2b1	92.27	91.86	92.27	91.99	89.04
CAI_ENv2b1	90.70	90.25	90.70	90.29	85.66
SEAI_ENv2b1	91.55	91.03	91.55	91.21	86.62
IA_ENv2b1	92.15	91.75	92.15	91.90	88.85
(WA0)
SAI_ENv2b2	92.27	92.20	92.27	91.998	87.13
CAI_ENv2b2	90.70	90.06	90.70	90.30	82.75
SEAI_ENv2b2	91.18	91.13	91.18	90.97	87.57
IA_ENv2b2	92.15	92.05	92.15	91.90	87.40
(WA0)
SAI_ENv2b3	92.03	91.72	92.03	91.71	85.68
CAI_ENv2b3	91.67	91.51	91.67	91.52	86.64
SEAI_ENv2b3	91.43	91.18	91.43	91.24	87.13
IA_ENv2b3	91.90	91.70	91.90	91.65	86.00
(WA0)
SAI_ENv2L	90.94	90.63	90.94	90.68	86.10
CAI_ENv2L	92.63	92.82	92.63	92.61	91.90
SEAI_ENv2L	89.98	91.13	89.98	90.39	91.32
IA_ENv2L	92.50	92.50	92.50	92.40	90.50
(WA0)
SAI_ENv2M	91.67	91.34	91.67	91.42	87.10
CAI_ENv2M	92.27	91.83	92.27	91.93	86.64
SEAI_ENv2M	92.03	91.80	92.03	91.55	88.02
IA_ENv2M	92.20	91.75	92.20	91.80	86.50
(WA0)
SAI_ENv2S	91.43	91.13	91.43	91.20	87.07
CAI_ENv2S	92.27	91.89	92.27	91.97	84.78
SEAI_ENv2S	90.82	90.90	90.82	90.33	86.06
IA_ENv2S	92.50	92.20	92.50	92.30	85.90
(WA0)
ISA	93.00	93.30	93.70	93.50	91.80
(WA1)
SSA	91.10	91.00	91.10	90.95	91.90
(WA1)
PSA	92.30	92.45	92.30	92.15	91.90
(WA1)
TASE	93.40	93.30	93.40	93.20	91.95
(WA2)
Ours	94.44	94.13	94.44	94.24	92.04

Table 19 compares the performance metrics of various classifiers using the Weighted Averaging (WA) technique at different levels with our proposed Cohen’s Kappa Proportioned Averaging (CKPA) approach. The results clearly show that CKPA consistently outperforms WA across all levels and evaluation metrics, demonstrating the effectiveness of the proposed method.

Regarding accuracy, the highest value achieved by the WA-based methods was 93.40%, obtained by the TASE model at the second level (WA₂). In contrast, the proposed method achieved an accuracy of 94.44%, surpassing the best WA result. This improvement highlights the superior capability of CKPA in correctly classifying instances.

Precision also favors CKPA. The highest precision among the WA methods was 93.30%, achieved by the TASE model at the second level (WA₂), whereas the proposed method achieved a precision of 94.13%. This indicates that CKPA is more effective in reducing false positives compared to conventional WA.

For recall, the best performance among WA methods was 93.70%, achieved by ISA at the first level (WA₁). The proposed method achieved a recall of 94.44%, outperforming the best WA result, which reflects its improved ability to correctly identify positive instances.

The F1-score further demonstrates the superiority of the proposed method. While the highest F1-score among WA-based approaches was 93.50% (achieved by ISA at WA₁ level), the proposed method achieved 94.24%, indicating a better balance between precision and recall.

In conclusion, the proposed CKPA approach consistently achieves superior performance across all evaluation metrics compared to the traditional Weighted Averaging technique. By addressing the limitations of WA, CKPA provides a more robust and reliable ensemble strategy for improving classification performance.

Based on the overall experimental analysis, it is evident that the integration of Triple-Attention (TA) and CKPA forms an effective and optimized architecture compared to existing ensemble methods.

Answers to the research questions

Computational overhead

The multi-layer CKPA framework, while effective, introduces considerable computational demands due to its reliance on multiple TASE architectures and iterative ensemble refinement. Both training and inference are impacted by the need to coordinate diverse attention modules and base classifiers, posing challenges for large-scale datasets or environments with limited computational resources.

Dependence on homogeneous base classifier

The success of CKPA depends on the diversity of its constituent models. Using homogeneous architectures may lead to overlapping feature representations, thereby limiting ensemble gains. Although this study leverages varied TASE forms (SSA, PSA, ISA) to mitigate this risk, incorporating additional architectural or algorithmic diversity could further improve robustness.

Dataset-specific generalization

The evaluation is based on a single benchmark dataset, which may contain domain-specific biases or distributional characteristics not representative of broader settings. Consequently, the model’s performance might decline when applied to cross-domain data, such as alternative imaging protocols or diverse lesion populations.

External validation limitation

Another limitation is the lack of external validation, as the proposed framework has been evaluated on a single benchmark dataset. This may restrict its immediate generalizability to real-world clinical settings. However, the primary contribution of this work is architectural, and future research will focus on validating the proposed model on multiple datasets as well as real-world clinical data to further assess its robustness and applicability.

Optimizing computational efficiency

Future research could aim to reduce the computational demands of the multi-layer CKPA framework while preserving its ensemble advantages. Strategies may include knowledge distillation to compress multiple TASE architectures into more compact models, dynamic pruning of redundant classifiers during inference, and hardware-aware parallelization to maximize resource utilization. Additionally, adaptive ensembling depth—where the number of CKPA layers is determined based on dataset complexity—could provide a balanced trade-off between computational cost and performance, facilitating real-world deployment.

Enforcing base classifier diversity

The success of CKPA depends on the diversity of its underlying models. Automated approaches for promoting classifier heterogeneity could be explored, such as adversarial decorrelation losses to reduce redundant feature learning, and neural architecture search (NAS) to identify optimal combinations of attention modules and transfer learning backbones. Furthermore, hybrid ensembles that integrate CNNs with transformer-based or graph neural network models could enrich feature representations, particularly for rare or morphologically complex lesion subtypes.

Cross-domain generalization and robustness

To enhance the framework’s applicability beyond a single dataset, future work should focus on validating CKPA performance across multi-center datasets with diverse imaging protocols, patient demographics, and lesion distributions. Incorporating domain adaptation techniques and evaluating on heterogeneous datasets will help address distribution shifts and improve generalizability. Establishing collaborative benchmarking with clinical partners can create standardized evaluation protocols for real-world challenges, such as low-quality images or streaming data with class imbalance. Additionally, integrating uncertainty quantification into the CKPA ensemble weighting process could enhance reliability in ambiguous cases, supporting greater trust and adoption in clinical settings.