Detection of Covid-19 from Chest CT Images using Xception Architecture: A Deep Transfer Learning based Approach
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, and causes a large number of deaths. Early dete...
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| Main Author: | |
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| Format: | Article |
| Language: | English |
| Published: |
Sakarya University
2021-06-01
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| Series: | Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi |
| Subjects: | |
| Online Access: | https://dergipark.org.tr/tr/download/article-file/1664654 |
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| Summary: | 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, and causes 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, image features are extracted using Xception network, convolutional neural network (CNN) based transfer learning architecture, 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 publicly available 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. 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. |
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| ISSN: | 2147-835X |