An explainable hybrid deep learning framework for precise skin lesion segmentation and multi-class classification - Fiaz, Muhammad; Shoaib Khan, Muhammad Bilal; Khan, Abdul Hannan; Bilal, Anas; Abdullah, Monir; Darem, Abdulbasit A.; Sarwar, Raheem (Frontiers in Medicine, 2025)
IntroductionSkin diseases, ranging from benign conditions to malignant tumors such as melanoma, present substantial diagnostic challenges due to their visual complexity and the inherent subjectivity in manual examination.MethodsThis paper introduces a hybrid deep learning framework specifically designed for skin lesion segmentation and multi-class classification using dermoscopic images. The proposed model integrates a dual-task architecture, which combines a U-Net-based segmentation network with a classification module based on the EfficientNet-B0 backbone. To improve model interpretability and foster clinical trust, Grad-CAM is incorporated, allowing clinicians to visualize heatmaps that highlight the regions influencing the model’s decisions.ResultsThe model was trained and evaluated on the HAM10000 dataset, demonstrating robust performance, with a Dice coefficient surpassing 0.85 for segmentation and classification accuracy nearing 85%. Despite challenges such as class imbalance and the variety of lesion types, the model provides reliable results across different skin conditions.DiscussionThe use of explainable AI (XAI) enhances transparency, a crucial factor in the clinical acceptance of AI-based diagnostic tools. This approach shows promise in improving diagnostic accuracy and supporting dermatologists, especially in resource-constrained settings, by providing both accurate lesion delineation and reliable class predictions. Future research will focus on improving the model’s generalizability, addressing underrepresented classes, and validating its effectiveness in real-world scenarios.