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

• Published in: Revista română de informatică și automatică = Romanian journal of information technology and automatic control Vol. 32; no. 3; pp. 65 - 72
• Main Authors: ALASSIRI, Raghad, ABUKHODAIR, Felwa, KALKATAWI, Manal, KHASHOGGI, Khalid, ALOTAIBI, Reem
• Format: Journal Article
• Language: English
• Published: ICI Publishing House 30.09.2022
• Subjects: covid-19; ct scan; data augmentation; deep learning models; transfer learning
• ISSN: 1220-1758; 1841-4303
• DOI: 10.33436/v32i3y202205

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 (https://covid-e46e8.web.app/) 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.