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The monkeypox virus can spread to humans but is not as dangerous as the smallpox virus. Monkeypox is usually found in the wild forests of Africa. Since the COVID-19 pandemic started, it has spread to more places around the world. Animals, like different kinds of rodents and nonhuman mammals, are the main hosts. People are now afraid of it because of how quickly it is spreading. They think it could be the next COVID-19. So, it is important to identify the virus at the initial stage before it starts spreading globally. In this work, we have developed several CNN models to identify the Monkeypox virus from other skin diseases. For this purpose, 6 CNN models were trained and compared, consisting of mainly VGG, DenseNet 121 and 201, Inception, Xception and MobileNet. Tensorflow was used along with the CNN models for better classification of the images. For this classification, two different datasets were used for training and testing, were a set of random images were used for the testing purpose. Inception V3 classified the disease with the precision of 94.99% and 89.97 as the F1-score.

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Deep Learning Approaches for Monkeypox Virus Prediction: A Comparative Study

  • 会议论文

  • 发表日期：

    2025年

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  • Someswar Pal,
  • Amit Kumar Mishra,
  • Kanad Ray,
  • Saurav Mallik

摘要

The monkeypox virus can spread to humans but is not as dangerous as the smallpox virus. Monkeypox is usually found in the wild forests of Africa. Since the COVID-19 pandemic started, it has spread to more places around the world. Animals, like different kinds of rodents and nonhuman mammals, are the main hosts. People are now afraid of it because of how quickly it is spreading. They think it could be the next COVID-19. So, it is important to identify the virus at the initial stage before it starts spreading globally. In this work, we have developed several CNN models to identify the Monkeypox virus from other skin diseases. For this purpose, 6 CNN models were trained and compared, consisting of mainly VGG, DenseNet 121 and 201, Inception, Xception and MobileNet. Tensorflow was used along with the CNN models for better classification of the images. For this classification, two different datasets were used for training and testing, were a set of random images were used for the testing purpose. Inception V3 classified the disease with the precision of 94.99% and 89.97 as the F1-score.