A convolutional neural network-based COVID-19 detection method using chest CT images

Yi Cao; Chen Zhang; Cheng Peng; Guangfeng Zhang; Yi Sun; Xiaoxue Jiang; Zhan Wang; Die Zhang; Lifei Wang; Jikui Liu

doi:10.21037/atm-22-534

A convolutional neural network-based COVID-19 detection method using chest CT images

Ann Transl Med. 2022 Mar;10(6):333. doi: 10.21037/atm-22-534.

Authors

Yi Cao^#¹, Chen Zhang^#¹, Cheng Peng¹, Guangfeng Zhang¹, Yi Sun¹, Xiaoxue Jiang¹, Zhan Wang¹, Die Zhang¹, Lifei Wang¹, Jikui Liu²

Affiliations

¹ Department of Radiology, National Clinical Research Center for Infectious Disease, Shenzhen Third People's Hospital, The Second Hospital Affiliated to Southern University of Science and Technology, Shenzhen, China.
² Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen College of Advanced Technology, University of the Chinese Academy of Sciences, Shenzhen, China.

^# Contributed equally.

Abstract

Background: High-throughput population screening for the novel coronavirus disease (COVID-19) is critical to controlling disease transmission. Convolutional neural networks (CNNs) are a cutting-edge technology in the field of computer vision and may prove more effective than humans in medical diagnosis based on computed tomography (CT) images. Chest CT images can show pulmonary abnormalities in patients with COVID-19.

Methods: In this study, CT image preprocessing are firstly performed using fuzzy c-means (FCM) algorithm to extracted the region of the pulmonary parenchyma. Through multiscale transformation, the preprocessed image is subjected to multi scale transformation and RGB (red, green, blue) space construction. After then, the performances of GoogLeNet and ResNet, as the most advanced CNN architectures, were compared in COVID-19 detection. In addition, transfer learning (TL) was employed to solve overfitting problems caused by limited CT samples. Finally, the performance of the models were evaluated and compared using the accuracy, recall rate, and F1 score.

Results: Our results showed that the ResNet-50 method based on TL (ResNet-50-TL) obtained the highest diagnostic accuracy, with a rate of 82.7% and a recall rate of 79.1% for COVID-19. These results showed that applying deep learning technology to COVID-19 screening based on chest CT images is a very promising approach. This study inspired us to work towards developing an automatic diagnostic system that can quickly and accurately screen large numbers of people with COVID-19.

Conclusions: We tested a deep learning algorithm to accurately detect COVID-19 and differentiate between healthy control samples, COVID-19 samples, and common pneumonia samples. We found that TL can significantly increase accuracy when the sample size is limited.

Keywords: Coronavirus disease 2019 (COVID-19); computed tomography (CT); convolutional neural network; transfer learning.