Abstract
Monkeypox virus is emerging slowly with the decline of COVID-19 virus infections around the world. People are afraid of it, thinking that it would appear as a pandemic like COVID-19. As such, it is crucial to detect them earlier before widespread community transmission. AI-based detection could help identify them at the early stage. In this paper, we aim to compare 13 different pre-trained deep learning (DL) models for the Monkeypox virus detection. For this, we initially fine-tune them with the addition of universal custom layers for all of them and analyse the results using four well-established measures: Precision, Recall, F1-score, and Accuracy. After the identification of the best-performing DL models, we ensemble them to improve the overall performance using a majority voting over the probabilistic outputs obtained from them. We perform our experiments on a publicly available dataset, which results in average Precision, Recall, F1-score, and Accuracy of 85.44%, 85.47%, 85.40%, and 87.13%, respectively with the help of our proposed ensemble approach. These encouraging results, which outperform the state-of-the-art methods, suggest that the proposed approach is applicable to health practitioners for mass screening.
Keywords: Classification; Deep learning; Detection; Monkeypox; Pandemic; SARS-Cov2.
【저자키워드】 pandemic, deep learning, detection, classification, SARS-CoV2., Monkeypox, 【초록키워드】 COVID-19, Infection, virus, Health, virus detection, COVID-19 virus, community transmission, dataset, experiment, early stage, average, help, Practitioner, widespread, approach, deep, fine-tune, IMPROVE, identify, detect, addition, majority, 【제목키워드】 deep,