COVID-19 diagnosis from chest CT scan images using deep learning

1 King Abdulaziz and His Companions foundation for Giftedness and Creativity (Mawhiba), Saudi Arabia
2 Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
3 Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia,,,,

Abstract: Coronavirus disease 2019 (COVID-19) has caused nearly 600 million individual infections worldwide and more than 6 million deaths were reported. With recent advancements in deep learning techniques, there have been significant efforts to detect and diagnose COVID-19 from computerized tomography (CT) scan medical images using deep learning. A retrospective study to detect COVID-19 using deep learning algorithms is conducted in this paper. It aims to improve training results of pre-trained models using transfer learning and data augmentation The performance of different models was measured and the difference in performance with and without using data augmentation was computed. Also, a Convolutional Neural Network (CNN) model was proposed and data augmentation was used to achieve high accuracy ratios. Finally, designed a website that uses the trained models where doctors can upload CT scan images and get COVID-19 classification ( was designed. The highest results from pre-trained models without using data augmentation were for DenseNet121, which was equal to 81.4%, and the highest accuracy after using the data augmentation was for MobileNet, which was equal to 83.4%. The rate of accuracy improvement percentage after using data augmentation was about 3%. The conclusion was that data augmentation could improve the accuracy of COVID-19 detection models as it increases the number of samples used to train these models.

Keywords: COVID-19, deep learning models, CT scan, data augmentation, transfer learning.

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Raghad ALASSIRI, Felwa ABUKHODAIR, Manal KALKATAWI, Khalid KHASHOGGI, Reem ALOTAIBI, COVID-19 diagnosis from chest CT scan images using deep learning, Romanian Journal of Information Technology and Automatic Control) ISSN 1220-1758, vol. 32(3), pp. 65-72, 2022.