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

Objective: Early recognition of skin lesions, including diverse abnormalities and life-threatening skin cancers, is critical for effective treatment and improved clinical outcomes. However, existing skin lesion datasets exhibit significant class imbalance, and there is no standardized guideline for optimal data augmentation strategies. This study aims to establish a robust and interpretable framework that addresses these limitations while enhancing diagnostic performance. Methods: We propose a novel transfer learning-based framework termed Results: Experimental validation on the HAM10000 dataset demonstrated that the proposed framework achieved a superior accuracy of 94.44%, outperforming several state-of-the-art methods. Grad-CAM visualizations were employed to enhance interpretability by highlighting lesion-relevant regions, thereby improving model transparency and reliability. Conclusion: The proposed TASE framework delivers enhanced diagnostic accuracy while effectively mitigating challenges related to class imbalance, dataset variability, and computational efficiency. By combining hierarchical triple-attention mechanisms with multi-layer ensemble weighting, it offers a reliable and interpretable solution for early and precise skin lesion classification, supporting real-world dermatological applications and improved patient care.