Deep learning Model for Recognizing Monkey Pox based on Dense net-121 Algorithm

While the world is trying to get rid of the Covid 19 pandemic, the beginning of the monkeypox(MPX) pandemic has recently appeared and is threatening many countries of the world. MPX is a rare disease caused by infection with the MPX virus, and it is among the same family of pox viruses. The danger is that MPX causes pustules all over the body, which causes a revolting view to the body regions and works as a source of infection in case of skin contact between individuals. Pustules and rashes are common symptoms of many pox viruses and other skin diseases such as Measles, chicken pox, syphilis, Eczema, etc, Therefore, the medical and clinical diagnosis of monkeypox is one of the great challenges for doctors and specialists. In response to this need, Artificial intelligence can develop aid systems based on machine and deep learning algorithms for diagnosing these types of diseases based on datasets of skin images to those types of diseases. In this paper, a deep learning approach called Dense Net-121model is applied, tested, and compared with the convolution neural network (CNN) model for diagnosing monkeypox through a skin image dataset of MPX and Measles images. The most significant finding to emerge from this study is the superiority of the Dense Net-121 model over CNN in diagnosing MPX cases with a testing accuracy of 93%. These findings suggest a role for using more deep learning algorithms for accurately diagnosing MPX cases with bigger datasets of similar pustules and rashes diseases.