Optimization of pneumonia CT classification model using RepVGG and spatial attention features

Qinyi Zhang; Jianhua Shu; Chen Chen; Zhaohang Teng; Zongyun Gu; Fangfang Li; Junling Kan

doi:10.3389/fmed.2023.1233724

Optimization of pneumonia CT classification model using RepVGG and spatial attention features

Front Med (Lausanne). 2023 Sep 19:10:1233724. doi: 10.3389/fmed.2023.1233724. eCollection 2023.

Authors

Qinyi Zhang¹, Jianhua Shu¹, Chen Chen¹, Zhaohang Teng¹, Zongyun Gu¹, Fangfang Li¹, Junling Kan¹

Affiliation

¹ School of Medical Information Engineering, Anhui University of Chinese Medicine, Hefei, China.

Abstract

Introduction: Pneumonia is a common and widespread infectious disease that seriously affects the life and health of patients. Especially in recent years, the outbreak of COVID-19 has caused a sharp rise in the number of confirmed cases of epidemic spread. Therefore, early detection and treatment of pneumonia are very important. However, the uneven gray distribution and structural intricacy of pneumonia images substantially impair the classification accuracy of pneumonia. In this classification task of COVID-19 and other pneumonia, because there are some commonalities between this pneumonia, even a small gap will lead to the risk of prediction deviation, it is difficult to achieve high classification accuracy by directly using the current network model to optimize the classification model.

Methods: Consequently, an optimization method for the CT classification model of COVID-19 based on RepVGG was proposed. In detail, it is made up of two essential modules, feature extraction backbone and spatial attention block, which allows it to extract spatial attention features while retaining the benefits of RepVGG.

Results: The model's inference time is significantly reduced, and it shows better learning ability than RepVGG on both the training and validation sets. Compared with the existing advanced network models VGG-16, ResNet-50, GoogleNet, ViT, AlexNet, MobileViT, ConvNeXt, ShuffleNet, and RepVGG_b0, our model has demonstrated the best performance in a lot of indicators. In testing, it achieved an accuracy of 0.951, an F1 score of 0.952, and a Youden index of 0.902.

Discussion: Overall, multiple experiments on the large dataset of SARS-CoV-2 CT-scan dataset reveal that this method outperforms most basic models in terms of classification and screening of COVID-19 CT, and has a significant reference value. Simultaneously, in the inspection experiment, this method outperformed other networks with residual structures.

Keywords: RepVGG; attention mechanism; classification model; optimization; pneumonia.