Deep learning for COVID-19 chest CT (computed tomography) image analysis: A lesson from lung cancer

Computational and Structural Biotechnology Journal

Shiming Tang¹  Hao Jiang²  Yang Zhang²  Weihuang Liu³ 

[1] School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 610054, China;College of Science, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, China;School of Computing and Engineering, University of Missouri-Kansas City, MO, United States;

关键词: COVID-19;    Lung cancer;    Chest CT image;    CycleGAN;    Image synthesis;    Style transfer;   

DOI  :  

来源: DOAJ

【 摘 要 】

As a recent global health emergency, the quick and reliable diagnosis of COVID-19 is urgently needed. Thus, many artificial intelligence (AI)-base methods are proposed for COVID-19 chest CT (computed tomography) image analysis. However, there are very limited COVID-19 chest CT images publicly available to evaluate those deep neural networks. On the other hand, a huge amount of CT images from lung cancer are publicly available. To build a reliable deep learning model trained and tested with a larger scale dataset, the proposed model builds a public COVID-19 CT dataset, containing 1186 CT images synthesized from lung cancer CT images using CycleGAN. Additionally, various deep learning models are tested with synthesized or real chest CT images for COVID-19 and Non-COVID-19 classification. In comparison, all models achieve excellent results in accuracy, precision, recall and F1 score for both synthesized and real COVID-19 CT images, demonstrating the reliable of the synthesized dataset. The public dataset and deep learning models can facilitate the development of accurate and efficient diagnostic testing for COVID-19.

【 授权许可】

Unknown