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

## TL;DR

XMP-Net is a modified Xception model that helps identify monkeypox and other skin diseases with high accuracy and provides visual explanations for its predictions.

## Contribution

The novel contribution is the integration of XAI techniques with a modified Xception model for interpretable monkeypox and skin disease classification.

## Key findings

- XMP-Net achieved 98.25% accuracy for monkeypox classification.
- Grad-CAM and LIME visualizations improved model interpretability for clinicians.
- The model showed high precision (91.80%) and F1-score (94.92%) for monkeypox detection.

## Abstract

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.