Detection of Covid-19 from Chest CT Images Using Xception Architecture: A Deep Transfer Learning Based Approach


Polat Ö.

Sakarya University Journal of Science, vol.25, no.3, pp.813-823, 2021 (Peer-Reviewed Journal)

  • Publication Type: Article / Article
  • Volume: 25 Issue: 3
  • Publication Date: 2021
  • Doi Number: 10.16984/saufenbilder.903886
  • Journal Name: Sakarya University Journal of Science
  • Journal Indexes: Academic Search Premier, Business Source Elite, Business Source Premier, TR DİZİN (ULAKBİM)
  • Page Numbers: pp.813-823

Abstract

Covid-19 infection, which first appeared in Wuhan, China in December 2019, affected the whole world in a short time like three months. The disease caused by the virus called SARS-CoV-2 affects many organs, especially the lungs, brain, liver and kidney, andcauses a large number of deaths. Early detection of Covid-19 using computer-aided methods will ensure that the patient reaches the right treatment without wasting time, and the spread of the disease will be controlled. This study proposes a solution for detecting Covid-19 using chest computed tomography (CT) scan images. Firstly, features are extracted by Xception network, convolutional neural network (CNN) based transfer learning method, then classification process is performed with a fully connected neural network (FCNN) added at the end of this architecture. The classification model was tested ten times on the accessible SARS-CoV-2-CT-scan dataset containing 2482 CT images labelled as covid and non-covid. The precision, recall, f1-score and accuracy metrics were used as performance measures; and ROC curve related to the model was drawn. While obtaining an average of 98.89% accuracy, in the best case, 99.59% classification performance was achieved. Xception outperforms other methods in the literature. The results promise that the proposed method can be evaluated as a clinical option helping experts in the detection of Covid-19 from CT images.