A Deep Learning Approach for Automated Skin Lesion Diagnosis with Explainable AI

High diagnostic accuracy achieved: The system demonstrated 91.15% overall accuracy in classifying seven types of skin lesions, with particularly strong performance on melanoma and melanocytic nevi detection, suggesting potential clinical utility for supporting dermatologists.

Explainability improves clinical trust: The researchers used explainable AI techniques (Grad-CAM and saliency maps) to show which visual features the model relied on for its diagnoses, making the "black box" decision-making process transparent and interpretable for healthcare professionals.

Data quality matters for fairness: The study employed data balancing and augmentation methods to handle the HAM10000 dataset, addressing a key responsible AI concern—ensuring the model performs equitably across all seven lesion classes rather than favoring common diagnoses.

XAI enables clinical validation: By visualizing which skin characteristics the model identified as important for each classification, the explainability component allows doctors to verify whether the AI is using medically relevant features, not spurious patterns, thereby supporting responsible deployment in healthcare.

Complementary tool, not replacement: The framing suggests this system is designed to support dermatologists' decision-making rather than replace human judgment, which aligns with responsible AI principles for high-stakes medical applications.