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

Monkeypox is a zoonotic infectious disease induced by the Monkeypox 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 monkeypox as a global public health pandemic, in July-2022. Timely diagnosis is imperative for assessing disease severity, conducting clinical evaluations, and determining suitable treatment plans. 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 during public health emergencies, particularly in the case of epidemics and pandemics. Therefore, this survey paper provides an extensive and efficient analysis of deep learning (DL) methods for the automatic detection of MP 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 MP. 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 study and is anticipated to serve as a path for researchers.