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ORIGINAL RESEARCH article

Front. Cell. Infect. Microbiol.
Sec. Virus and Host
Volume 14 - 2024 | doi: 10.3389/fcimb.2024.1397316

MpoxNet: Dual-Branch Deep Residual Squeeze and Excitation monkeypox classification Network with attention mechanism Provisionally Accepted

  • 1School of Electronic Information, Xijing University, China
  • 2Shaanxi Key Laboratory of Integrated and Intelligent Navigation, China
  • 3Xi’an Key Laboratory of High Precision Industrial Intelligent Vision Measurement Technology, China

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While the world struggles to recover from the devastation wrought by the widespread spread of COVID-19, monkeypox virus has emerged as a new global pandemic threat. In this paper, a high precision and lightweight classification network MpoxNet based on ConvNext is proposed to meet the need of fast and safe detection of monkeypox classification. In this method, a two-branch depthseparable convolution residual Squeeze and Excitation module is designed. This design aims to extract more feature information with two branches, and greatly reduces the number of parameters in the model by using depth-separable convolution. In addition, our method introduces a convolutional attention module to enhance the extraction of key features within the receptive field. The experimental results show that MpoxNet has achieved remarkable results in monkeypox disease classification, the accuracy rate is 95.28%, the precision rate is 96.40%, the recall rate is 93.00%, and the F1-Score is 95.80%. This is significantly better than the current mainstream classification model. It is worth noting that the FLOPS and the number of parameters of MpoxNet are only 30.68% and 31.87% of those of ConvNext-Tiny, indicating that the model has a small computational burden and model complexity while efficient performance. in a laboratory in Copenhagen, Denmark (Ladnyj et al., 1972) and is known as Mpox due to its similar outbreak symptoms to smallpox.The Mpox virus caused the first infection in the Congo in 1970. Since then, most cases have occurred in Congo, Central and West Africa, and the number of cases has gradually increased, affecting many people living near tropical regions. As of 2022, the World Health Organization(WHO) reports that several other non-African countries such as Europe and the United States have also reported cases of Mpox virus infection (Alakunle et al., 2020).Since the declaration of the eradication of smallpox in 1980 and the subsequent cessation of smallpox vaccination, monkeypox has emerged as the predominant orthopoxvirus. Its symptoms resemble those of smallpox, thus garnering attention in the field of public health (Mohbey et al., 2022).In 2003, the United States became the first country outside Africa to experience a monkeypox outbreak.

Keywords: Monkeypox, deep learning, image processing, artificial intelligence, Feature Selection

Received: 07 Mar 2024; Accepted: 08 May 2024.

Copyright: © 2024 Sun, Yuan, Sun, Zhu, Deng, Gong and Chen. 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: Dr. Baoxi Yuan, School of Electronic Information, Xijing University, Xi’an, China