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 project designs transfer learning based modified VGG16 and Custom CNN algorithm to predict Monkeypox disease as this disease is not deadly but spreading very fastly. To deal with this disease for timely detection doctors can use this algorithm for detection. Normal SKIN and Monkeypox images are uses to train both algorithms. The existing VGG16 gives low accuracy, and the proposed custom CNN gives high accuracy.

Description