南方科技大学知识苑(SUSTech KC): A convolutional neural network-based COVID-19 detection method using chest CT images

题名	A convolutional neural network-based COVID-19 detection method using chest CT images
作者	Cao, Yi1 ; Zhang, Chen 1 ; Peng, Cheng 1 ; Zhang, Guangfeng 1; Sun, Yi 1; Jiang, Xiaoxue 1; Wang, Zhan 1 ; Zhang, Die 1; Wang, Lifei 1; Liu, Jikui 2
通讯作者	Wang, Lifei; Liu, Jikui
发表日期	2022-03-01
DOI	10.21037/atm-22-534
发表期刊	Annals of Translational Medicine 影响因子和分区
ISSN	2305-5839
EISSN	2305-5847
摘要	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.
关键词	Coronavirus disease 2019 (COVID-19) computed tomography (CT) convolutional neural network transfer learning
相关链接	[来源记录]
收录类别	SCI
语种	英语
学校署名	第一 ; 通讯
WOS研究方向	Oncology ; Research & Experimental Medicine
WOS类目	Oncology ; Medicine, Research & Experimental
WOS记录号	WOS:000777859500001
出版者	AME PUBL CO
来源库	Web of Science
引用统计	被引频次[WOS]：4
成果类型	期刊论文
条目标识符	http://sustech.caswiz.com/handle/2SGJ60CL/329284
专题	南方科技大学第二附属医院南方科技大学第一附属医院
作者单位	1.Southern Univ Sci & Technol, Natl Clin Res Ctr Infect Dis, Shenzhen Peoples Hosp 3, Dept Radiol,Hosp 2, 29 Bulan Rd, Shenzhen 518000, Peoples R China 2.Univ Chinese Acad Sci, Chinese Acad Sci, Shenzhen Coll Adv Technol, Shenzhen Inst Adv Technol, Shenzhen, Peoples R China
第一作者单位	南方科技大学第二附属医院; 南方科技大学第一附属医院
通讯作者单位	南方科技大学第二附属医院; 南方科技大学第一附属医院
第一作者的第一单位	南方科技大学第二附属医院; 南方科技大学第一附属医院
推荐引用方式 GB/T 7714	Cao, Yi,Zhang, Chen,Peng, Cheng,et al. A convolutional neural network-based COVID-19 detection method using chest CT images[J]. Annals of Translational Medicine,2022.
APA	Cao, Yi.,Zhang, Chen.,Peng, Cheng.,Zhang, Guangfeng.,Sun, Yi.,...&Liu, Jikui.(2022).A convolutional neural network-based COVID-19 detection method using chest CT images.Annals of Translational Medicine.
MLA	Cao, Yi,et al."A convolutional neural network-based COVID-19 detection method using chest CT images".Annals of Translational Medicine (2022).