Explainable AI-Driven Detection of Human Monkeypox Using Deep Learning and Vision Transformers: A Comprehensive Analysis

TL;DR

This study evaluates deep learning and vision transformer models for detecting human monkeypox from skin images, highlighting dataset limitations and demonstrating the effectiveness of transfer learning and explainable AI techniques.

Contribution

It introduces a comprehensive analysis of deep learning and vision transformer models for monkeypox detection, emphasizing the benefits of transfer learning and explainability.

Findings

MobileNet-v2 achieved 93.15% accuracy.

Transfer learning improved classifier performance.

Explainable AI techniques validated model decisions.

Abstract

Since mpox can spread from person to person, it is a zoonotic viral illness that poses a significant public health concern. It is difficult to make an early clinical diagnosis because of how closely its symptoms match those of measles and chickenpox. Medical imaging combined with deep learning (DL) techniques has shown promise in improving disease detection by analyzing affected skin areas. Our study explore the feasibility to train deep learning and vision transformer-based models from scratch with publicly available skin lesion image dataset. Our experimental results show dataset limitation as a major drawback to build better classifier models trained from scratch. We used transfer learning with the help of pre-trained models to get a better classifier. The MobileNet-v2 outperformed other state of the art pre-trained models with 93.15% accuracy and 93.09% weighted average F1 score. ViT B16 and ResNet-50 also achieved satisfactory performance compared to already available studies with accuracy 92.12% and 86.21% respectively. To further validate the performance of the models, we applied explainable AI techniques.