Concerns about public health have been heightened by the rapid spread of monkeypox to more than 90 countries. To contain the spread, AI assisted diagnosis system can play an important role. In this study, different deep CNN models with multiple machine learning classifiers are investigated for monkepox disease diagnosis using skin images. For this, bottleneck features of three CNN models i.e. AlexNet, GoogleNet and Vgg16Net are exploited with multiple machine learning classifiers such as SVM, KNN, Naive Bayes, Decision Tree and Random Forest. Results shows that with Vgg16Net features, Naive Bayes classifier gives highest accuracy of 91.11%.

The authors have declared no competing interest.

This study did not receive any funding

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