Monkey Pox Disease Detecting System Using Deep Learning

Due of the recent monkey pox outbreak's quick spread to more than 40 nations outside of Africa, public health is now at risk. Monkey pox can be difficult to diagnose clinically in its early stages since it resembles both chickenpox and measles. Computer-assisted monkey pox lesion detection may be useful for surveillance and quick identification of suspected cases when confirmatory Polymerase Chain Reaction (PCR) assays are not easily accessible. Under the condition that there are enough training examples available, deep learning techniques have been demonstrated to be useful in the automated detection of skin lesions. Such databases are not, however, currently accessible for the monkey pox condition. In the present research first, we create the "Monkey pox Skin Lesion Dataset (MSLD)," which includes pictures of skin lesions caused by measles, chickenpox, and monkeypox. The majority of the photographs come from news websites, blogs, and publicly available case reports. A 3-fold cross-validation experiment is set up, and the sample size is increased through data augmentation. The second phase is categorising diseases like monkeypox using a number of pre-trained deep learning models, including VGG-16, CNN, and Mobile Net.