AI-Based Approaches for the Diagnosis of Mpox: Challenges and Future Prospects

Abstract

Mpox, a zoonotic viral disease, poses a significant threat to human health, characterized by its potential for human-to-human transmission and its manifestation in severe flu-like symptoms and distinctive skin lesions. This paper offers a comprehensive exploration of Mpox detection and classification, beginning with an introduction to the subject and a description of the research objectives and scope. A thorough examination of the historical context and epidemiology of Mpox sets the stage for a detailed discussion of the fundamental background concepts, encompassing medical imaging, various types of medical imaging techniques, machine learning (ML) applications, convolutional neural networks (CNNs), and available architectural families. The study highlights essential model evaluation metrics to provide a robust framework for assessing the efficacy of different approaches. Methodologically, the paper outlines the systematic approach employed in the literature review and study selection process. With an emphasis on benchmark datasets, the research delves into the diverse AI-based methodologies, encompassing both ML and deep learning (DL) approaches, utilized in Mpox detection. The paper meticulously describes the challenges inherent in these methodologies and concludes with a thoughtful exploration of future prospects in the field. The main purpose is to provide a comprehensive overview of the current landscape and pave the way for advancements that can significantly impact the diagnosis and management of Mpox outbreaks.