H-MpoxNet: A Hybrid Deep Learning Framework for Mpox Detection from Image Data

Abstract

Infectious diseases can create significant global threats to public health and economic stability by creating pandemics. SARS-CoV-2 is a recent example. Early detection of infectious diseases is crucial to prevent global outbreaks. Mpox, a contagious viral disease first detected in humans in 1970, has experienced multiple outbreaks in recent decades, which emphasizes the development of tools for its early detection. In this paper, we develop a hybrid deep learning framework for Mpox detection. This framework allows us to construct hybrid deep learning models combining deep learning architectures as a feature extraction tool with Machine Learning classifiers and perform a comprehensive analysis of Mpox detection from image data. Our best-performing model consists of MobileNetV2 with LightGBM classifier, which achieves an accuracy of 91.49%, 91.87% weighted precision, 91.49% weighted recall, 91.51% weighted F1-score and Matthews Correlation Coefficient score of 0.83.