Automatic distinction between COVID-19 and common pneumonia using multi-scale convolutional neural network on chest CT scans

Tao Yan, Pak Kin Wong, Hao Ren, Huaqiao Wang, Jiangtao Wang and Yang Li

Chaos, Solitons & Fractals, 2020, vol. 140, issue C

Abstract: The COVID-19 pneumonia is a global threat since it emerged in early December 2019. Driven by the desire to develop a computer-aided system for the rapid diagnosis of COVID-19 to assist radiologists and clinicians to combat with this pandemic, we retrospectively collected 206 patients with positive reverse-transcription polymerase chain reaction (RT-PCR) for COVID-19 and their 416 chest computed tomography (CT) scans with abnormal findings from two hospitals, 412 non-COVID-19 pneumonia and their 412 chest CT scans with clear sign of pneumonia are also retrospectively selected from participating hospitals. Based on these CT scans, we design an artificial intelligence (AI) system that uses a multi-scale convolutional neural network (MSCNN) and evaluate its performance at both slice level and scan level. Experimental results show that the proposed AI has promising diagnostic performance in the detection of COVID-19 and differentiating it from other common pneumonia under limited number of training data, which has great potential to assist radiologists and physicians in performing a quick diagnosis and mitigate the heavy workload of them especially when the health system is overloaded. The data is publicly available for further research at https://data.mendeley.com/datasets/3y55vgckg6/1https://data.mendeley.com/datasets/3y55vgckg6/1.

Keywords: COVID-19 pneumonia; Artificial intelligence; Mutile-scale convolutional neural network; Computed tomography (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:140:y:2020:i:c:s096007792030549x

DOI: 10.1016/j.chaos.2020.110153

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