Monkeypox Classification based on Skin Images using CNN: EfficientNet-B0

Abstract

Monkeypox is a zoonotic infectious disease caused by a virus of the orthopoxvirus genus. It can infect humans, vertebrates, and arthropods. Transmission to humans occurs through direct contact with infected animal body fluids or consumption of undercooked meat. Monkeypox cases have been reported globally, with thousands of confirmed cases and several deaths. Early symptoms include fever, rash, swollen lymph nodes, back pain, and headache. Diagnosis can be made through physical examination and laboratory tests. Imagebased artificial intelligence technology, specifically the EfficientNet-B0 architecture, has been proposed as a solution for the classification of monkeypox based on skin lesion images. The research aims to compare the performance of EfficientNetB0 with other CNN architectures and contribute to the development of medical image classification technology. Among the models evaluated, the EfficientNet-B0 model emerged as the standout performer, achieving an accuracy of 85.12%, surpassing the accuracy of other models such as MobileNet (63.63%) and InceptionV3 (71.4%). EfficientNet-B0 also demonstrated strong sensitivity (78.46%) and impressive specificity (91.78%), outperforming other models in these metrics. Additionally, despite not surpassing the accuracy of ResNet-50 (87.59%), EfficientNet-B0 achieved its accuracy with approximately four times fewer parameters, highlighting its efficiency in parameter usage and computational resources. These results can help improve models and aid in clinical decision-making.