Contribution of artificial intelligence applications developed with the deep learning method to the diagnosis of COVID-19 pneumonia on computed tomography
Yıl: 2021 Cilt: 69 Sayı: 4 Sayfa Aralığı: 486 - 491 Metin Dili: İngilizce DOI: 10.5578/tt.20219606 İndeks Tarihi: 29-05-2022
Contribution of artificial intelligence applications developed with the deep learning method to the diagnosis of COVID-19 pneumonia on computed tomography
Öz: Contribution of artificial intelligence applications developed with the deep
learning method to the diagnosis of COVID-19 pneumonia on computed
tomography
Introduction: Computed tomography (CT) is an auxiliary modality in the
diagnosis of the novel Coronavirus (COVID-19) disease and can guide physicians in the presence of lung involvement. In this study, we aimed to investigate the contribution of deep learning to diagnosis in patients with typical
COVID-19 pneumonia findings on CT.
Materials and Methods: This study retrospectively evaluated 690 lesions obtained from 35 patients diagnosed with COVID-19 pneumonia based on typical
findings on non-contrast high-resolution CT (HRCT) in our hospital. The diagnoses of the patients were also confirmed by other necessary tests. HRCT
images were assessed in the parenchymal window. In the images obtained,
COVID-19 lesions were detected. For the deep Convolutional Neural
Network (CNN) algorithm, the Confusion matrix was used based on a
Tensorflow Framework in Python.
Results: A total of 596 labeled lesions obtained from 224 sections of the images were used for the training of the algorithm, 89 labeled lesions from 27
sections were used in validation, and 67 labeled lesions from 25 images in
testing. Fifty-six of the 67 lesions used in the testing stage were accurately
detected by the algorithm while the remaining 11 were not recognized. There
was no false positive. The Recall, Precision and F1 score values in the test
group were 83.58, 1, and 91.06, respectively. Conclusion: We successfully detected the COVID-19 pneumonia lesions on CT images using the algorithms created with artificial
intelligence. The integration of deep learning into the diagnostic stage in medicine is an important step for the diagnosis of diseases
that can cause lung involvement in possible future pandemics.
Anahtar Kelime: Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık
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| APA | Aydın N, Çelik Ö (2021). Contribution of artificial intelligence applications developed with the deep learning method to the diagnosis of COVID-19 pneumonia on computed tomography. , 486 - 491. 10.5578/tt.20219606 |
| Chicago | Aydın Nevin,Çelik Özer Contribution of artificial intelligence applications developed with the deep learning method to the diagnosis of COVID-19 pneumonia on computed tomography. (2021): 486 - 491. 10.5578/tt.20219606 |
| MLA | Aydın Nevin,Çelik Özer Contribution of artificial intelligence applications developed with the deep learning method to the diagnosis of COVID-19 pneumonia on computed tomography. , 2021, ss.486 - 491. 10.5578/tt.20219606 |
| AMA | Aydın N,Çelik Ö Contribution of artificial intelligence applications developed with the deep learning method to the diagnosis of COVID-19 pneumonia on computed tomography. . 2021; 486 - 491. 10.5578/tt.20219606 |
| Vancouver | Aydın N,Çelik Ö Contribution of artificial intelligence applications developed with the deep learning method to the diagnosis of COVID-19 pneumonia on computed tomography. . 2021; 486 - 491. 10.5578/tt.20219606 |
| IEEE | Aydın N,Çelik Ö "Contribution of artificial intelligence applications developed with the deep learning method to the diagnosis of COVID-19 pneumonia on computed tomography." , ss.486 - 491, 2021. 10.5578/tt.20219606 |
| ISNAD | Aydın, Nevin - Çelik, Özer. "Contribution of artificial intelligence applications developed with the deep learning method to the diagnosis of COVID-19 pneumonia on computed tomography". (2021), 486-491. https://doi.org/10.5578/tt.20219606 |
| APA | Aydın N, Çelik Ö (2021). Contribution of artificial intelligence applications developed with the deep learning method to the diagnosis of COVID-19 pneumonia on computed tomography. Tüberküloz ve Toraks, 69(4), 486 - 491. 10.5578/tt.20219606 |
| Chicago | Aydın Nevin,Çelik Özer Contribution of artificial intelligence applications developed with the deep learning method to the diagnosis of COVID-19 pneumonia on computed tomography. Tüberküloz ve Toraks 69, no.4 (2021): 486 - 491. 10.5578/tt.20219606 |
| MLA | Aydın Nevin,Çelik Özer Contribution of artificial intelligence applications developed with the deep learning method to the diagnosis of COVID-19 pneumonia on computed tomography. Tüberküloz ve Toraks, vol.69, no.4, 2021, ss.486 - 491. 10.5578/tt.20219606 |
| AMA | Aydın N,Çelik Ö Contribution of artificial intelligence applications developed with the deep learning method to the diagnosis of COVID-19 pneumonia on computed tomography. Tüberküloz ve Toraks. 2021; 69(4): 486 - 491. 10.5578/tt.20219606 |
| Vancouver | Aydın N,Çelik Ö Contribution of artificial intelligence applications developed with the deep learning method to the diagnosis of COVID-19 pneumonia on computed tomography. Tüberküloz ve Toraks. 2021; 69(4): 486 - 491. 10.5578/tt.20219606 |
| IEEE | Aydın N,Çelik Ö "Contribution of artificial intelligence applications developed with the deep learning method to the diagnosis of COVID-19 pneumonia on computed tomography." Tüberküloz ve Toraks, 69, ss.486 - 491, 2021. 10.5578/tt.20219606 |
| ISNAD | Aydın, Nevin - Çelik, Özer. "Contribution of artificial intelligence applications developed with the deep learning method to the diagnosis of COVID-19 pneumonia on computed tomography". Tüberküloz ve Toraks 69/4 (2021), 486-491. https://doi.org/10.5578/tt.20219606 |
