Classification of Monkeypox Virus Skin Lesions Based on Improved ResNet

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    Monkeypox virus is currently circulating globally and is clinically indistinguishable from other skin diseases, particularly the smallpox virus and chickenpox virus. In the case that deterministic polymerase chain reaction technology and other biological detection technologies are not fully mature, it is a feasible method to detect skin lesions caused by the monkeypox virus by computer-aided diagnostic technology, so a classification algorithm for skin lesions caused by the monkeypox virus based on the residual network is proposed. Based on the residual network, the algorithm combines deep separable convolution and lightweight attention, which reduces the computational amount and complexity of the model and improves the classification performance of the model. The experimental results show that the algorithm shows excellent classification performance for skin lesions caused by the monkeypox virus, and the classification accuracy, recall, and precision of skin lesions caused by the monkeypox virus are 97.3%, 96.8%, and 97.2%, respectively, which are better than those of the common classification models and other research methods used in the experiment.

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  • Received:December 09,2022
  • Revised:January 17,2023
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  • Online: April 25,2023
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