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As the world is still trying to rebuild from the destruction caused by thewidespread reach of the COVID-19 virus, and the recent alarming surge of humanmonkeypox disease outbreaks in numerous countries threatens to become a newglobal pandemic too. Human monkeypox disease syndromes are quite similar tochickenpox, and measles classic symptoms, with very intricate differences suchas skin blisters, which come in diverse forms. Various deep-learning methodshave shown promising performances in the image-based diagnosis of COVID-19,tumor cell, and skin disease classification tasks. In this paper, we try tointegrate deep transfer-learning-based methods, along with a convolutionalblock attention module (CBAM), to focus on the relevant portion of the featuremaps to conduct an image-based classification of human monkeypox disease. Weimplement five deep-learning models, VGG19, Xception, DenseNet121,EfficientNetB3, and MobileNetV2, along with integrated channel and spatialattention mechanisms, and perform a comparative analysis among them. Anarchitecture consisting of Xception-CBAM-Dense layers performed better than theother models at classifying human monkeypox and other diseases with avalidation accuracy of 83.89%.

Classification of Human Monkeypox Disease Using Deep Learning Models and Attention Mechanisms