Hierarchical attention stacked ensemble with Matthews-correlation-coefficient weighted averaging: A novel framework for skin lesion classification.

Objective: Early and accurate identification of skin lesions-ranging from benign irregularities to life-threatening cancers-is crucial for improving clinical outcomes. However, existing skin lesion datasets suffer from severe class imbalance, and there is limited consensus on effective augmentation strategies. This study aims to develop a robust framework that mitigates these limitations while enhancing diagnostic accuracy and interpretability. Methods: We introduce a novel transfer learning-based framework termed Results: Experimental evaluations on the HAM10000 dataset demonstrated that the proposed framework achieved an outstanding accuracy of 93.96%, surpassing several state-of-the-art approaches. The use of Grad-CAM visualizations further enhanced model interpretability by effectively localizing lesion-relevant regions. Conclusion: The proposed HASE framework not only delivers superior diagnostic accuracy but also alleviates challenges associated with class imbalance, limited dataset diversity, and high computational cost. By combining hierarchical attention and multi-level ensemble weighting, it establishes a reliable and interpretable solution for early and precise skin lesion classification, offering significant potential for real-world dermatological applications and improved patient care.