Automatic detection of coronavirus disease (COVID-19) in X-ray and CT images: A machine learning based approach

• Autorzy: Kassani Sara Hosseinzadeh , Kassani Peyman Hosseinzadeh , Wesolowski Michal J. , Schneider Kevin A. , Deters Ralph

Identyfikatory

DOI

10.1016/j.bbe.2021.05.013

• Języki publikacji: EN

Abstrakty

EN

The newly identified Coronavirus pneumonia, subsequently termed COVID-19, is highly transmittable and pathogenic with no clinically approved antiviral drug or vaccine available for treatment. The most common symptoms of COVID-19 are dry cough, sore throat, and fever. Symptoms can progress to a severe form of pneumonia with critical complications, including septic shock, pulmonary edema, acute respiratory distress syndrome and multi-organ failure. While medical imaging is not currently recommended in Canada for primary diagnosis of COVID-19, computer-aided diagnosis systems could assist in the early detection of COVID-19 abnormalities and help to monitor the progression of the disease, potentially reduce mortality rates. In this study, we compare popular deep learningbased feature extraction frameworks for automatic COVID-19 classification. To obtain the most accurate feature, which is an essential component of learning, MobileNet, DenseNet, Xception, ResNet, InceptionV3, InceptionResNetV2, VGGNet, NASNet were chosen amongst a pool of deep convolutional neural networks. The extracted features were then fed into several machine learning classifiers to classify subjects as either a case of COVID-19 or a control. This approach avoided task-specific data pre-processing methods to support a better generalization ability for unseen data. The performance of the proposed method was validated on a publicly available COVID-19 dataset of chest X-ray and CT images. The DenseNet121 feature extractor with Bagging tree classifier achieved the best performance with 99% classification accuracy. The second-best learner was a hybrid of the a ResNet50 feature extractor trained by LightGBM with an accuracy of 98%.

Słowa kluczowe

EN

coronavirus disease; lung opacity; computer aided diagnosis; deep learning; feature extraction; transfer learning

PL

koronawirus; zmętnienie płuc; diagnoza wspomagana komputerowo; uczenie głębokie; ekstrakcja cech; transfer wiedzy

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2021
• Tom: Vol. 41, no. 3
• Strony: 867--879
• Opis fizyczny: Bibliogr. 57 poz., rys., tab.

Twórcy

autor

Kassani Sara Hosseinzadeh

• Department of Computer Science, University of Saskatchewan, Canada

autor

Kassani Peyman Hosseinzadeh

• Department of Neurology and Neurological, University of Stanford, USA

autor

Wesolowski Michal J.

• Department of Medical Imaging, University of Saskatchewan, Canada

autor

Schneider Kevin A.

• Department of Computer Science, University of Saskatchewan, Canada

autor

Deters Ralph

• Department of Computer Science, University of Saskatchewan, Canada

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).