A hybrid deep learning framework for early detection of Mpox using image data

Infectious diseases pose significant global threats to public health and economic stability by causing pandemics. 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 epidemics in recent decades, emphasizing the development of tools for its early detection. In this paper, we propose 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%, precision of 86.96%, weighted precision of 91.87%, recall of 95.24%, weighted recall of 91.49%, F1 score of 90.91%, weighted F1-score of 91.51% and Matthews Correlation Coefficient score of 0.83. • Develop a hybrid deep learning framework for early detection of Mpox from image data. • Integrate deep learning architectures with machine learning classifiers for improved detection accuracy. • Achieve 91.49% accuracy and high precision, recall, and F1 score in Mpox detection. • Use MobileNetV2 and LightGBM classifier for optimal performance in Mpox image analysis. • Show the effectiveness of hybrid models in combating infectious disease outbreaks.