4.7 Article

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

Journal

COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL
Volume 19, Issue -, Pages 1391-1399

Publisher

ELSEVIER
DOI: 10.1016/j.csbj.2021.02.016

Keywords

COVID-19; Lung cancer; Chest CT image; CycleGAN; Image synthesis; Style transfer; Classification

Funding

  1. Shenzhen Science and Technol-ogy Innovation Commission (Shenzhen Basic Research Project) [JCYJ20180306172131515]

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To address the urgent need for COVID-19 diagnosis, AI-based methods for analyzing chest CT images have been proposed. By synthesizing a dataset and testing various deep learning models, accurate and efficient diagnostic testing for COVID-19 can be achieved.
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. (C) 2021 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.

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