A Recent Survey of the Advancements in Deep Learning Techniques for Monkeypox Disease Detection

• URL: http://arxiv.org/abs/2311.10754v2
• Date: Thu, 23 Nov 2023 20:08:49 GMT
• Title: A Recent Survey of the Advancements in Deep Learning Techniques for Monkeypox Disease Detection
• Authors: Saddam Hussain Khan, Rashid Iqbal, Saeeda Naz (Artifical Intelligence Lab, Department of Computer Systems Engineering, University of Engineering and Applied Science (UEAS), Swat, Pakistan)
• Abstract summary: Monkeypox (MPox) is a zoonotic infectious disease induced by the MPox Virus. This survey paper provides an extensive analysis of deep learning (DL) methods for the automatic detection of MPox in skin lesion images.
• Score: 0.7179624965454197
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Monkeypox (MPox) is a zoonotic infectious disease induced by the MPox Virus, part of the poxviridae orthopoxvirus group initially discovered in Africa and gained global attention in mid-2022 with cases reported outside endemic areas. Symptoms include headaches, chills, fever, smallpox, measles, and chickenpox-like skin manifestations and the WHO officially announced MPox as a global public health pandemic, in July 2022.Traditionally, PCR testing of skin lesions is considered a benchmark for the primary diagnosis by WHO, with symptom management as the primary treatment and antiviral drugs like tecovirimat for severe cases. However, manual analysis within hospitals poses a substantial challenge including the substantial burden on healthcare professionals, limited facilities, availability and fatigue among doctors, and human error during public health emergencies. Therefore, this survey paper provides an extensive and efficient analysis of deep learning (DL) methods for the automatic detection of MPox in skin lesion images. These DL techniques are broadly grouped into categories, including deep CNN, Deep CNNs ensemble, deep hybrid learning, the newly developed, and Vision transformer for diagnosing MPox. Moreover, this study offers a systematic exploration of the evolutionary progression of DL techniques and identifies, and addresses limitations in previous methods while highlighting the valuable contributions and innovation. Additionally, the paper addresses benchmark datasets and their collection from various authentic sources, pre-processing techniques, and evaluation metrics. The survey also briefly delves into emerging concepts, identifies research gaps, limitations, and applications, and outlines challenges in the diagnosis process. This survey furnishes valuable insights into the prospective areas of DL innovative ideas and is anticipated to serve as a path for researchers.

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