MonkeyNet: A robust deep convolutional neural network for monkeypox disease detection and classification. (April 2023)

• Record Type: Journal Article
• Authors: Bala, Diponkor; Hossain, Md. Shamim; Hossain, Mohammad Alamgir; Abdullah, Md. Ibrahim; Rahman, Md. Mizanur; Manavalan, Balachandran; Gu, Naijie; Islam, Mohammad S.; Huang, Zhangjin
• Is Part Of: Neural networks. Volume 161(2023)
• Page Start: 757
• Page End: 775
• Publication Date: 2023-04
• Subjects: Monkeypox disease -- Dataset -- Machine learning -- Deep learning -- Convolutional neural network -- Classification; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2023.02.022
• Languages: English
• ISSNs: 0893-6080

Abstract

Abstract: The monkeypox virus poses a new pandemic threat while we are still recovering from COVID-19. Despite the fact that monkeypox is not as lethal and contagious as COVID-19, new patient cases are recorded every day. If preparations are not made, a global pandemic is likely. Deep learning (DL) techniques are now showing promise in medical imaging for figuring out what diseases a person has. The monkeypox virus-infected human skin and the region of the skin can be used to diagnose the monkeypox early because an image has been used to learn more about the disease. But there is still no reliable Monkeypox database that is available to the public that can be used to train and test DL models. As a result, it is essential to collect images of monkeypox patients. The " MSID " dataset, short form of " Monkeypox Skin Images Dataset ", which was developed for this research, is free to use and can be downloaded from the Mendeley Data database by anyone who wants to use it. DL models can be built and used with more confidence using the images in this dataset. These images come from a variety of open-source and online sources and can be used for research purposes without any restrictions. Furthermore, we proposed and evaluated a modified DenseNet-201 deep learning-based CNN model named MonkeyNet. Using the original and augmented datasets, this study suggested a deep convolutional neural network that was able to correctly identify monkeypox disease with an accuracy of 93.19% and 98.91% respectively. This implementation also shows the Grad-CAM which indicates the level of the model's effectiveness and identifies the infected regions in each class image, which will help the clinicians. The proposed model will also help doctors make accurate early diagnoses of monkeypox disease and protect against the spread of the disease. 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.