Interpretable Deep Learning for Monkeypox Lesion Classification: A Study Using Model-Agnostic Explainability Techniques

The rapid spread of Monkeypox has underscored the importance of accurate and reliable diagnostic tools, particularly dermatological assessment. This research introduces a deep-learning method to accurately classify Monkeypox skin lesions, emphasizing transparency through model-agnostic explainability techniques. To achieve this, we incorporated local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) into our analytical framework. We developed a Convolutional Neural Network (CNN) model to accurately distinguish Monkeypox skin lesions, utilizing a selected dataset of high-resolution lesion images. LIME was employed to generate detailed explanations for individual predictions, helping to pinpoint the exact features within the lesion images that most strongly influenced the model’s decisions. Additionally, SHAP was utilized to assess the contribution of every feature to the model’s overall predictions. It provides comprehensive information on the model’s behaviour and ensures consistency during decision-making. The insights derived from these explainability methods were crucial in validating the model’s reliability and interpreting misclassification instances, which informed subsequent model refinements. Quantitative results demonstrated that our model not only achieved a high accuracy of $\mathbf{9 2. 1 9 \%}$ but also concentrated on clinically significant areas of the lesions, as verified by LIME and SHAP visualizations. These findings suggest that integrating LIME and SHAP within deep learning frameworks can greatly enhance AI-powered diagnostic tools’ trustworthiness and clinical relevance. Future research will aim to extend this approach to other dermatological conditions and investigate its potential integration into clinical decision-making processes.