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J Healthc Eng ; 2020: 8843664, 2020.
Article in English | MEDLINE | ID: covidwho-729435

ABSTRACT

Coronavirus Disease (COVID19) is a fast-spreading infectious disease that is currently causing a healthcare crisis around the world. Due to the current limitations of the reverse transcription-polymerase chain reaction (RT-PCR) based tests for detecting COVID19, recently radiology imaging based ideas have been proposed by various works. In this work, various Deep CNN based approaches are explored for detecting the presence of COVID19 from chest CT images. A decision fusion based approach is also proposed, which combines predictions from multiple individual models, to produce a final prediction. Experimental results show that the proposed decision fusion based approach is able to achieve above 86% results across all the performance metrics under consideration, with average AUROC and F1-Score being 0.883 and 0.867, respectively. The experimental observations suggest the potential applicability of such Deep CNN based approach in real diagnostic scenarios, which could be of very high utility in terms of achieving fast testing for COVID19.


Subject(s)
Betacoronavirus , Clinical Laboratory Techniques/statistics & numerical data , Coronavirus Infections/diagnostic imaging , Deep Learning , Neural Networks, Computer , Pneumonia, Viral/diagnostic imaging , Tomography, X-Ray Computed/statistics & numerical data , Biomedical Engineering , Coronavirus Infections/diagnosis , Coronavirus Infections/epidemiology , Databases, Factual , Humans , Pandemics , Pneumonia, Viral/epidemiology , Thorax/diagnostic imaging
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