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ORIGINAL RESEARCH article
Front. Med.
Sec. Precision Medicine
Volume 11 - 2024 |
doi: 10.3389/fmed.2024.1443812
This article is part of the Research Topic Application of Deep Learning in Biomedical Image Processing View all articles
Using Novel Deep Learning Models for Rapid and Efficient Assistance in Monkeypox Screening from Skin Images
Provisionally accepted- 1 Jiangsu University, Zhenjiang, China
- 2 Dalian Medical University, Dalian, Liaoning, China
- 3 Fudan University, Shanghai, Shanghai Municipality, China
- 4 Tsinghua University, Beijing, Beijing, China
- 5 China Academy of Chinese Medical Sciences, Beijing, Beijing Municipality, China
- 6 Third Affiliated Hospital of Nantong University, Nantong, China
Monkeypox, a communicable disease instigated by the monkeypox virus, transmits through direct contact with infectious skin lesions or mucosal blisters, posing severe complications such as pneumonia, encephalitis, and even fatality. Traditional clinical diagnostics, heavily reliant on the discerning judgement of clinical experts, are both time-consuming and labor-intensive, with inherent infection risks, underscoring the critical need for automated, efficient auxiliary diagnostic models. In response, we have developed a deep learning classification model augmented by self-attention mechanisms and feature pyramid integration, employing attentional strategies to amalgamate image features across varying scales and assimilating a priori knowledge from the VGG model to selectively capture salient features. Aiming to enhance task performance and model generalizability, we incorporated different components into the baseline model in a series of ablation studies, revealing the contribution of each component to overall model efficacy. In comparison with state-of-the-art deep learning models, our proposed model achieved the highest accuracy and precision, marking a 6% improvement over the second-best model. The results from ablation experiments corroborate the effectiveness of individual module components in enhancing model performance. Our method for diagnosing monkeypox demonstrates improved diagnostic precision and extends the reach of medical services in resource-constrained settings.
Keywords: Monkeypox, deep learning, Self-attention mechanisms, Auxiliary Diagnostic, Skin images
Received: 04 Jun 2024; Accepted: 26 Aug 2024.
Copyright: © 2024 Deng, Liu, Kong, Zang, Hu and Zou. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Meiyin Zou, Third Affiliated Hospital of Nantong University, Nantong, China
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