Explainable COVID-19 Detection Using Chest CT Scans and Deep Learning.
Sensors (Basel)
; 21(2)2021 Jan 11.
Article
in English
| MEDLINE | ID: covidwho-1022007
ABSTRACT
This paper explores how well deep learning models trained on chest CT images can diagnose COVID-19 infected people in a fast and automated process. To this end, we adopted advanced deep network architectures and proposed a transfer learning strategy using custom-sized input tailored for each deep architecture to achieve the best performance. We conducted extensive sets of experiments on two CT image datasets, namely, the SARS-CoV-2 CT-scan and the COVID19-CT. The results show superior performances for our models compared with previous studies. Our best models achieved average accuracy, precision, sensitivity, specificity, and F1-score values of 99.4%, 99.6%, 99.8%, 99.6%, and 99.4% on the SARS-CoV-2 dataset, and 92.9%, 91.3%, 93.7%, 92.2%, and 92.5% on the COVID19-CT dataset, respectively. For better interpretability of the results, we applied visualization techniques to provide visual explanations for the models' predictions. Feature visualizations of the learned features show well-separated clusters representing CT images of COVID-19 and non-COVID-19 cases. Moreover, the visualizations indicate that our models are not only capable of identifying COVID-19 cases but also provide accurate localization of the COVID-19-associated regions, as indicated by well-trained radiologists.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Thorax
/
Tomography, X-Ray Computed
/
Deep Learning
/
COVID-19
Type of study:
Diagnostic study
/
Prognostic study
Limits:
Humans
Language:
English
Year:
2021
Document Type:
Article
Affiliation country:
S21020455
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