TY  - JOUR
TI  - Contribution of artificial intelligence
applications developed with the deep learning
method to the diagnosis of COVID-19
pneumonia on computed tomography
AB  - 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.
AU  - Aydın, Nevin
AU  - Çelik, Özer
DO  - 10.5578/tt.20219606
PY  - 2021
JO  - Tüberküloz ve Toraks
VL  - 69
IS  - 4
SN  - 0494-1373
SP  - 486
EP  - 491
DB  - TRDizin
UR  - http://search/yayin/detay/511021
ER  -