COVID-19 classification on CT image using lightweight RG DenseNet
10.3969/j.issn.1005-202X.2023.12.007
- VernacularTitle:基于轻量级RG-DenseNet的COVID-19CT图像分类
- Author:
Ziyu ZHANG
1
;
Kehui ZHAO
;
Huifang NIU
;
Zhiqiang ZHANG
;
Liantian ZHOU
Author Information
1. 山东中医药大学智能与信息工程学院,山东济南 250000
- Keywords:
RepGhost;
DenseNet;
COVID-19;
deep learning;
image classification
- From:
Chinese Journal of Medical Physics
2023;40(12):1494-1501
- CountryChina
- Language:Chinese
-
Abstract:
Objective To construct a COVID-19 CT image classification model based on lightweight RG DenseNet.Methods A RG-DenseNet model was constructed by adding channel and spatial attention modules to DenseNet121 for minimizing the interference of irrelevant features,and replacing Bottleneck module in DenseNet with pre-activated RG beneck2 module for reducing model parameters while maintaining accuracy as much as possible.The model performance was verified with 3-category classification experiments on the COVIDx CT-2A dataset.Results RG-DenseNet had an accuracy,precision,recall rate,specificity,and F1-score of 98.93%,98.70%,98.97%,99.48%,and 98.83%,respectively.Conclusion Compared with the original model DenseNet121,RG-DenseNet reduces the number of parameters and the computational complexity by 92.7%,while maintaining an accuracy reduction of only 0.01%,demonstrating a significant lightweight effect and high practical application value.
