Abstract: Accurate classification of skin diseases is an important step toward early diagnosis and therapy. However, deep learning models are frequently used in therapeutic contexts without transparency, reducing confidence and acceptance. This study introduces SkinDiseaseXAI, a unique convolutional neural network (CNN) that uses Grad-CAM++ to classify ten different types of skin diseases and provide visual explanations. The proposed model was trained using a publicly available dataset of dermatoscopic images following preprocessing and augmentation. SkinDiseaseXAI achieved 76.12% training accuracy and 66.25% validation accuracy in 20 epochs. We used Grad-CAM++ to generate heatmaps that highlighted discriminative regions inside the lesion areas, thereby improving interpretability. The experimental results indicate that the model has the ability not only to perform multi-class skin disease categorization but also to provide interpretable visual outputs, which improves the transparency and dependability of decision-making processes. This concept has the possibility to improve clinical diagnosis by merging performance and explainability.

Ammar Nasser Alqarni and Abdullah Sheikh, “SkinDiseaseXAI: XAI-Driven Neural Networks for Skin Disease Detection” International Journal of Advanced Computer Science and Applications(IJACSA), 16(6), 2025. http://dx.doi.org/10.14569/IJACSA.2025.0160612

@article{Alqarni2025,
title = {SkinDiseaseXAI: XAI-Driven Neural Networks for Skin Disease Detection},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160612},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160612},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {6},
author = {Ammar Nasser Alqarni and Abdullah Sheikh}
}