Deep Learning Methods for Early Detection of Monkeypox Skin Lesion

ABSTRACT

As the world has not fully recovered from the aftermath of COVID-19, a new pandemic appears on the horizon. Monkey Pox is emerging as a new threat to the health of the world population. With the recorded spread over 40 countries worldwide it might be soon declared a pandemic. Monkey Pox shares common features with chickenpox and measles making it difficult to diagnose. Developing a new test kit at this early stage is a challenging task for the medical fraternity. This paper proposes the use of deep learning models that can be used to make the process of diagnosis automated. This paper tries to come up with a performance comparison of ResNet50, EfficientNetB3 and EfficientNetB7 algorithms. This study suggests a method for early detection of Monkey Pox Skin Lesion. Though an extensive study with other models on a larger dataset containing more images from various countries of the world needs to be carried out but this study gives some promising results on this limited dataset. © 2022 IEEE.