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.
Birincil Dil | İngilizce |
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Konular | Yapay Zeka, Mühendislik |
Bölüm | Araştırma Makalesi |
Yazarlar | |
Yayımlanma Tarihi | 30 Haziran 2021 |
Gönderilme Tarihi | 1 Nisan 2021 |
Kabul Tarihi | 5 Mayıs 2021 |
Yayımlandığı Sayı | Yıl 2021 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.