Detection of monkeypox disease from skin lesion images using Mobilenetv2 architecture

Monkeypox has recently become an endemic disease that threatens the whole world. The most distinctive feature of this disease is occurring skin lesions. However, in other types of diseases such as chickenpox, measles, and smallpox skin lesions can also be seen. The main aim of this study was to quickly detect monkeypox disease from others through deep learning approaches based on skin images. In this study, MobileNetv2 was used to determine in images whether it was monkeypox or non-monkeypox. To find splitting methods and optimization methods, a comprehensive analysis was performed. The splitting methods included training and testing (70:30 and 80:20) and 10 fold cross validation. The optimization methods as adaptive moment estimation (adam), root mean square propagation (rmsprop), and stochastic gradient descent momentum (sgdm) were used. Then, MobileNetv2 was tasked as a deep feature extractor and features were obtained from the global pooling layer. The Chi-Square feature selection method was used to reduce feature dimensions. Finally, selected features were classified using the Support Vector Machine (SVM) with different kernel functions. In this study, 10 fold cross validation and adam were seen as the best splitting and optimization methods, respectively, with an accuracy of 98.59%. Then, significant features were selected via the Chi-Square method and while classifying 500 features with SVM, an accuracy of 99.69% was observed.