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

• Published in: Computational and structural biotechnology journal Vol. 19; pp. 1391 - 1399
• Main Authors: Jiang, Hao, Tang, Shiming, Liu, Weihuang, Zhang, Yang
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.01.2021; Research Network of Computational and Structural Biotechnology; Elsevier
• Subjects: Chest CT image; Classification; COVID-19; CycleGAN; Image synthesis; Lung cancer; Style transfer; COVID-19; Style transfer; Image synthesis; Chest CT image; Lung cancer; Classification; CycleGAN
• ISSN: 2001-0370; 2001-0370
• DOI: 10.1016/j.csbj.2021.02.016

[Display omitted] 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.