MonkeyNet: A robust deep convolutional neural network for monkeypox disease detection and classification

Diponkor Bala, Md Shamim Hossain, Mohammad Alamgir Hossain, Md. Ibrahim Abdullah, Md. Mizanur Rahman, Balachandran Manavalan, Naijie Gu, Mohammad S. Islam, Zhangjin Huang

Abstract: Highlights•A first-ever multiclass image-based database has been developed named “Monkeypox Skin Images Dataset” for the detection and classification of monkeypox disease.•A new modified DenseNet-201 based deep CNN model named “MonkeyNet” has been proposed.•The proposed model has been evaluated with precision, recall, F1-score, accuracy, and AUC metrics and obtained 94.04% and 99.85% accuracy on original and augmented datasets respectively.•Deep learning and machine learning approaches have been applied to the developed dataset also.•A real-time framework has been developed based on the proposed method that helps clinicians to make quick and effective diagnoses and treatments.