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 hierarchical attention stacked ensemble (HASE), which integrates multiple EfficientNetV1 backbones through three distinct stacking schemes: HASE: independent TA, HASE: serial stacked TA, and HASE: parallel stacked TA. Here, TA denotes the triplet-attention module encompassing soft attention, channel attention, and squeeze-excitation mechanisms. To address prediction fusion, we propose an advanced ensemble aggregation method—Matthews-correlation-coefficient weighted averaging (MWA)—further extended into a multi-level MWA (ML-MWA) formulation. Additionally, four augmentation strategies were systematically evaluated to identify the most effective ensemble configuration.

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.

Search results

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