Automatic diagnosis of COVID-19 from CT images using CycleGAN and transfer learning

Navid Ghassemi; Afshin Shoeibi; Marjane Khodatars; Jonathan Heras; Alireza Rahimi; Assef Zare; Yu-Dong Zhang; Ram Bilas Pachori; J Manuel Gorriz

doi:10.1016/j.asoc.2023.110511

Automatic diagnosis of COVID-19 from CT images using CycleGAN and transfer learning

Appl Soft Comput. 2023 Sep:144:110511. doi: 10.1016/j.asoc.2023.110511. Epub 2023 Jun 13.

Authors

Navid Ghassemi^{1

2}, Afshin Shoeibi^{1

2}, Marjane Khodatars³, Jonathan Heras⁴, Alireza Rahimi², Assef Zare⁵, Yu-Dong Zhang⁶, Ram Bilas Pachori⁷, J Manuel Gorriz^{8

9}

Affiliations

¹ Faculty of Electrical Engineering, FPGA Lab, K. N. Toosi University of Technology, Tehran, Iran.
² Computer Engineering department, Ferdowsi University of Mashhad, Mashhad, Iran.
³ Department of Medical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran.
⁴ Department of Mathematics and Computer Science, University of La Rioja, La Rioja, Spain.
⁵ Faculty of Electrical Engineering, Gonabad Branch, Islamic Azad University, Gonabad, Iran.
⁶ School of Informatics, University of Leicester, Leicester, LE1 7RH, UK.
⁷ Department of Electrical Engineering, Indian Institute of Technology Indore, Indore 453552, India.
⁸ Department of Signal Theory, Networking and Communications, Universidad de Granada, Spain.
⁹ Department of Psychiatry, University of Cambridge, UK.

Abstract

The outbreak of the corona virus disease (COVID-19) has changed the lives of most people on Earth. Given the high prevalence of this disease, its correct diagnosis in order to quarantine patients is of the utmost importance in the steps of fighting this pandemic. Among the various modalities used for diagnosis, medical imaging, especially computed tomography (CT) imaging, has been the focus of many previous studies due to its accuracy and availability. In addition, automation of diagnostic methods can be of great help to physicians. In this paper, a method based on pre-trained deep neural networks is presented, which, by taking advantage of a cyclic generative adversarial net (CycleGAN) model for data augmentation, has reached state-of-the-art performance for the task at hand, i.e., 99.60% accuracy. Also, in order to evaluate the method, a dataset containing 3163 images from 189 patients has been collected and labeled by physicians. Unlike prior datasets, normal data have been collected from people suspected of having COVID-19 disease and not from data from other diseases, and this database is made available publicly. Moreover, the method's reliability is further evaluated by calibration metrics, and its decision is interpreted by Grad-CAM also to find suspicious regions as another output of the method and make its decisions trustworthy and explainable.

Keywords: COVID-19; CT scan; CycleGAN; Deep learning; Transfer learning.