Monkeypox disease has been declared a PHEIC by WHO to encourage coordinated efforts to contain the spread of the disease and protect communities. Although Monkeypox has a recovery period of 2 to 4 weeks, severe cases can be fatal, with a fatality ratio estimated at 3% to 6%. Monkeypox is not highly contagious but has been spreading rapidly in recent months, making it crucial to track and isolate suspected cases promptly. To aid in identifying and controlling the disease's spread, this study will create a large-scale dataset of Monkeypox disease images using GANs, which have proven effective in generating high-quality images. These images will be used to train deep learning models for image classification to diagnose Monkeypox disease accurately. The models' performance will be compared and evaluated using infected human skin images and healthy skin images to improve the accuracy of previous works, aiding in early diagnosis and treatment. Monkeypox is a disease that is typically transmitted through close contact with an infected person and is not considered to be highly contagious. However, in May 2022, there was a notable increase in the number of reported cases of the disease, suggesting that it was spreading rapidly. As a result, it is critical to quickly identify and isolate individuals who are suspected of having the disease in order to prevent further transmission. This can be achieved through effective contact tracing and prompt isolation of cases, which is essential for containing the spread of the disease and preventing it from becoming an epidemic. This study aims to assist in the identification and control of Monkeypox disease by creating a vast collection of Monkeypox disease images using Generative Adversarial Networks (GANs). GANs can create high-quality images, which will be used to develop an extensive and diverse dataset. This dataset will train various deep learning models for image classification to diagnose Monkeypox disease. The performance of the deep learning models will be assessed by comparing their accuracy in distinguishing infected human skin with Monkeypox disease from healthy skin images. The study's results can enhance the accuracy of previous methods and contribute to the early detection and treatment of Monkeypox disease.

Deep Learning Neural Network, Monkeypox Disease Datasets, Generative Adversarial Network.