Morphology-guided attention networks for explainable skin cancer detection under clinical uncertainty.

Accurate and reliable skin cancer detection from dermoscopic images remains challenging due to large visual variability, overlapping lesion appearances, and inherent clinical uncertainty. To address these issues, this work proposes a morphology-guided attention framework for explainable and uncertainty-aware skin lesion classification. The system integrates lesion segmentation to preserve clinically meaningful morphological structures, followed by an attention-based classification network that emphasizes diagnostically relevant regions while suppressing background artifacts. Visual attention and attribution maps are generated to provide transparent explanations aligned with established dermoscopic criteria. In addition, an uncertainty estimation module is incorporated to quantify prediction confidence and identify ambiguous or out-of-distribution cases for safe clinical triage. The proposed approach is evaluated on publicly available dermoscopic datasets and achieves classification accuracy 99.12% with a recall rate above 99% for malignant lesions, demonstrating strong sensitivity for early cancer detection. Experimental results show that morphology-guided attention improves both classification performance and interpretability compared to conventional deep learning models. Furthermore, uncertainty-aware predictions enhance model reliability by reducing overconfident errors in challenging cases. These findings indicate that the proposed framework offers a robust, explainable, and clinically relevant solution for automated skin cancer screening under real-world conditions.