Abstract

Info

Author(s):: ZHANG Ziyu1; ZHAO Kehui2; NIU Huifang3; ZHANG Zhiqiang1; ZHOU Liantian4; 1. College of Intelligence and Information Engineering, Shandong University of Traditional Chinese Medicine, Jinan 250000, China 2. Special Inspection Department, the Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan 250000, China 3. Shandong Province Adverse Drug Reaction Testing Center, Jinan 250000, China 4. Department of Lithotripsy, Heze Traditional Chinese Medicine Hospital, Heze 247000, China

Keywords:: Keywords: RepGhost DenseNet COVID-19 deep learning image classification

Abstract:: 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.