Systematic Study of Deep Learning Models for Image-Based Detection of Monkeypox Virus

• Published in: 2023 12th International Conference On Software Process Improvement (CIMPS) pp. 225 - 233
• Main Authors: Llamas, Vanessa Melenciano, Trejo, Miguel Angel de la Rosa, Castorena, Hugo Geovani Arroyo, Belmonte, Isaul Ibarra, Gonzalez, Ezra Federico Parra
• Format: Conference Proceeding
• Language: English; Spanish
• Published: IEEE 18.10.2023
• Subjects: convolutional neural networks; Deep learning; deep learning models; detection; Infectious diseases; Measurement; monkeypox; Reviews; Software; systematic review; Systematics
• DOI: 10.1109/CIMPS61323.2023.10528817

This article systematically reviews the progress of current research on monkeypox and presents the deep learning models used and shown in articles published in recent years for the detection of this infectious disease. We searched scientific databases and selected three articles that met the established criteria. These used image datasets and applied deep learning models, such as convolutional neural networks, to detect monkeypox in humans. The importance of using large datasets and combining different databases to improve the performance and generalizability of the models is highlighted. The results were evaluated using precision and accuracy metrics. It is concluded that there is significant growth in monkeypox detection research and provides recommendations for future research in this field.