Explainable AI for skin disease classification using gradient-weighted class activation mapping and transfer learning in digital health to identify contours

The study addresses a range of skin disorders, including acne, vitiligo, hyperpigmentation, nail psoriasis, and SJS-TEN, focusing on aspects that remain underexplored in previous research. The findings highlight the potential of simple data augmentation techniques; moreover, explainable AI: Grad-CAM interpreted the model outcome by showing image contours visually and identifying uncommon skin conditions and overcoming the data scarcity challenge. The implications of these research findings are significant for the development of machine learning-based diagnostic systems in the medical field. Further investigation is necessary to explore the generalizability of these findings to other medical datasets.