A Comprehensive Review on Monkeypox Skin Lesion Recognition through Deep Learning

Abstract

This comprehensive review delves into the emerging field of Monkeypox skin lesion recognition using deep learning techniques. Monkeypox, a rare viral disease with symptoms resembling smallpox, presents a diagnostic challenge, particularly in resource-limited regions. The paper explores the recent advancements in deep learning methodologies applied to the automated identification and classification of Monkeypox skin lesions, offering a detailed analysis of various neural network architectures, image preprocessing techniques, and dataset considerations. The review highlights the potential of deep learning models in enhancing the accuracy and efficiency of Monkeypox diagnosis, paving the way for improved early detection and timely intervention in affected populations. Additionally, it discusses challenges and future directions in this domain, emphasizing the need for robust and interpretable models to facilitate widespread adoption in clinical settings.