Early Detection of Monkeypox Skin Disease Using Patch Based DL Model and Transfer Learning Techniques

Abstract

In the field of medicine, it is very important to prognosticate diseases early to cure them from their initial stages. Monkeypox is a viral zoonosis with symptoms similar to the smallpox as it spreads widely with the person who is in close contact with the affected. So, it can be diagnosed using various new age computing techniques such as CNN, RESNET, VGG, EfficientNet. In this work, a prediction model is utilized for better classification of Monkeypox. However, the implementation of machine learning in detecting COVID-19 has encouraged scientists to explore its potential for identifying monkeypox. One challenge in using Deep learning (DL) and machine learning (ML) for this purpose is the lack of sufficient data, including images of monkeypox-infected skin. In response, Monkeypox Skin Image Dataset is collected from Kaggle, the largest of its kind till date which includes images of healthy skin as well as monkeypox and some other infected skin diseases. The dataset undergoes through different data augmentation phases which is fed to different DL and ML algorithms for producing better results. Out of all the approaches, VGG19 and Resnet has got the best result with 92% recognition accuracy.