Analysis of CNN features with multiple machine learning classifiers in diagnosis of monkepox from digital skin images
KUMAR, V.
Show abstract
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 monkeypox 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%.
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