A comparison of deep neural network architectures for COVID-19 detection using CT chest images

Sarıoğlu, Mehmet Tunahan
Coronavirus Disease 2019 (COVID-19) has rapidly spread around the world since December 2019. Due to the low confidence in the real-time reverse transcription-polymerase chain reaction (RT-PCR) test, which is considered the gold standard, CT images are frequently consulted for diagnosis. CT findings of COVID-19 patients has been analysed and documented in the literature, which are groundglass opacities (GGOs), air bronchograms, vascular enlargement and halo sign. But, these CT findings encountered in COVID-19 disease are not very specific and vary during the course of the disease. In addition, it has common CT appearance and symptoms with many other infectious and non-infectious diseases. This thesis compares various deep transfer learning structures used to distinguish COVID- 19 from other diseases using image processing techniques. In this study, the performances of the classification methods using five different convolutional neural network structures were compared. Unlike other similar studies, two public CT dataset was used together to train, test and validate the methods. Overfitting issues with the train data had been experienced and best scores are obtained using augmented data. Accuracy values of 81.65%, 86.08%, 90.82%, 79.75% and 81.01% were obtained in Xception, VGG 16, ResNet 50, Inception v3 and Inception ResNet v2 convolutional neural networks, respectively. The importance of hyper parameters such as epoch number, loss and activation functions used during training is also mentioned in this study.


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Citation Formats
M. T. Sarıoğlu, “A comparison of deep neural network architectures for COVID-19 detection using CT chest images,” M.S. - Master of Science, Middle East Technical University, 2022.