Abstract

The recent monkeypox outbreak has become a public health concern due to its rapid spread in more than 40 countries outside Africa. Clinical diagnosis of monkeypox in an early stage is challenging due to its similarity with chickenpox and measles. In cases where the confirmatory Polymerase Chain Reaction (PCR) tests are not readily available, computer-assisted detection of monkeypox lesions could be beneficial for surveillance and rapid identification of suspected cases. Deep learning methods have been found effective in the automated detection of skin lesions, provided that sufficient training examples are available. However, as of now, such datasets are not available for the monkeypox disease. This paper focused on implementation of transfer learning based modified VGG16 and Multi-layer convolutional neural network (ML-CNN) algorithms to predict Monkeypox disease from skin images. The dataset preprocessing is carried out to remove the various noises and normalizes the images. Finally, the simulations revealed that the proposed ML-CNN resulted in superior performance as compared to VGG16 model.

Description