Monkeypox datasets creation using GANs & image classification.

Monkeypox disease was declared a Public Health Emergency of International Concern (PHEIC) by the World Health Organization (WHO) on July 23rd, 2022. The purpose of this designation is to encourage the global community to take coordinated measures to contain the spread of the disease and protect communities. The recovery period for Monkeypox disease typically ranges from 2 to 4 weeks, and in some cases, the disease can resolve spontaneously with or without treatment. However, severe cases have been reported, which can lead to fatalities. The fatality ratio for Monkeypox disease is estimated between 3% to 6% at present. It is essential to take precautionary measures to prevent the spread of this disease and seek medical attention if any symptoms are observed. Monkeypox is not highly contagious, and it generally spreads through close contact with an infected person. However, in May 2022, a significant number of cases of Monkeypox disease were identified, indicating that the disease was spreading rapidly. In light of this situation, it is crucial to track and isolate suspected cases promptly to prevent further transmission. Rapid and effective contact tracing, along with timely isolation of cases, is essential to contain the spread of the disease and prevent it from becoming an epidemic. Identifying, controlling, and taking measures to control the spread of Monkeypox disease is of utmost importance. To aid in this effort, this paper aims to create a large-scale dataset of Monkeypox disease images using Generative Adversarial Networks (GANs). GANs have proven to be highly effective in generating high-quality images and can aid in creating a more comprehensive and diverse dataset. The generated images will be used to train different deep learning models for image classification to diagnose Monkeypox disease. The deep learning models will be compared, and their performance will be evaluated using images of infected human skin with Monkeypox disease and healthy skin images. The results obtained from this paper will improve the accuracy of previous works, aiding in the early diagnosis and treatment of Monkeypox disease. [ABSTRACT FROM AUTHOR]