Medical Image Classification for Monkeypox Case using Deep Learning Algorithms: A Survey
Ahmed Islam*1, Mohamed G. Abdelfattah 2, El-Sayed M. El-Kenawy 3, Hossam El-Din Moustafa4
1,3 Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura 35111, Egypt
2 Department of Electronics and Communications Engineering, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt
4 Department of Electronics and Communications Engineering, Faculty of Engineering, Mansoura University, Egypt
Emails: ahmedeslam@std.mans.edu.eg; eng.mo.gamal@mans.edu.eg; skenawy@ieee.org; hossam_moustafa@mans.edu.eg
Abstract
Due to the importance of maintaining public health and preventing the spread of diseases, nowadays, new diseases have spread at a lot of countries called Monkeypox after the world get rid of covid-19.it is crucial to diagnose Monkeypox and stop the spread of this disease. so that we make this review to give a point of view to Monkeypox spread nowadays. We have recently done nine research to overlay it with different artificial intelligence deep learning methods to diagnose Monkeypox from digital skin images due primarily to AI's success in COVID-19 identification. The VGG16, VGG19, ResNet50, ResNet101, DenseNet201, and AlexNet models were used in our proposed method to classify patients with monkeypox symptoms with other diseases of a similar kind (chickenpox, measles, and normal)., Due to the importance of facing this disease and summarizing these researches according to: methodology and results of detection accuracy, precision.
Keywords: artificial intelligence; Monkeypox; deep learning; Data collection; data augmentation.