The monkeypox virus is gradually reemerging as COVID-19 viral infections decline globally. The recent outbreak in the early 2022 had grown fear and concern in people, making them believe that it would spread like COVID-19. Public health organizations struggled to contain the present outbreak as healthcare professionals from all over the world tried to become familiar with the many clinical signs and treatment of this infection. Early identification is, therefore, a crucial step to stop them from spreading broadly throughout society. AI-based detection may be able to locate them at an early stage. Such detection and prediction techniques have brought about optimistic outcomes. It also implies that the proposed approach is appropriate for mass screening by health practitioners. In image processing-based diagnostics, such as detection of cancel, identification of tumor cell, and COVID-19 patient detection, machine learning has lately demonstrated great promise. Therefore, as monkeypox spreads to human skin, a similar technique might be used to detect it. To help with the condition's subsequent diagnosis, a picture can be obtained and a dataset containing similar images of the infection can be utilized to develop a near-exact prediction model. In this review, we discuss the clinical diagnosis, management, and improvement of the classification accuracy of monkeypox images for development of prediction models using ML. We also discuss the historical and contemporary outbreaks of the monkeypox virus. This study is being conducted in light of the ongoing epidemics in various parts of the world.
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Monkeypox Prediction using Machine Learning
Semantic Scholar · Computer Science · 2023
Abstract
The monkeypox virus is gradually reemerging as COVID-19 viral infections decline globally. The recent outbreak in the early 2022 had grown fear and concern in people, making them believe that it would spread like COVID-19. Public health organizations struggled to contain the present outbreak as healthcare professionals from all over the world tried to become familiar with the many clinical signs and treatment of this infection. Early identification is, therefore, a crucial step to stop them from spreading broadly throughout society. AI-based detection may be able to locate them at an early stage. Such detection and prediction techniques have brought about optimistic outcomes. It also implies that the proposed approach is appropriate for mass screening by health practitioners. In image processing-based diagnostics, such as detection of cancel, identification of tumor cell, and COVID-19 patient detection, machine learning has lately demonstrated great promise. Therefore, as monkeypox spreads to human skin, a similar technique might be used to detect it. To help with the condition's subsequent diagnosis, a picture can be obtained and a dataset containing similar images of the infection can be utilized to develop a near-exact prediction model. In this review, we discuss the clinical diagnosis, management, and improvement of the classification accuracy of monkeypox images for development of prediction models using ML. We also discuss the historical and contemporary outbreaks of the monkeypox virus. This study is being conducted in light of the ongoing epidemics in various parts of the world.