XMP-Net: An XAI-Based Modified Xception Model for Recognizing Monkeypox and Other Skin Diseases

This research introduces "XMP-Net," a modified Xception-based deep learning architecture constructed for the categorization of skin conditions, with a particular focus on identifying monkeypox. The study recognizes skin images of four categories: normal, chickenpox, measles, and monkeypox. To enhance interpretability and foster confidence in the model's predictions, Grad-CAM (gradient-weighted class activation mapping) and LIME (local interpretable model-agnostic explanations) were employed to illustrate the model's thinking manner. The model demonstrated impressive classification performance, attaining an accuracy of 98.33% for normal skin, 98.25% for monkeypox, 84.21% for measles, and 77.27% for chickenpox. Precision, recall, and F1-score values were also analyzed for each class, with monkeypox achieving a precision of 91.80%, a recall of 98.25%, and an F1-score of 94.92%. The visual explanations generated by Grad-CAM and LIME highlighted critical parts in the input images that affected the model's likelihoods, offering clinicians valuable insights into the diagnostic process. This research underscores the potential of explainable artificial intelligence (XAI) in augmenting traditional diagnostic methods, particularly for emerging communicable maladies like monkeypox, and provides a foundation for developing reliable, interpretable, and accessible diagnostic tools for resource-constrained settings.