Improving effectiveness of different deep learning-based models for detecting COVID-19 from computed tomography (CT) images


NEURAL COMPUTING & APPLICATIONS, vol.33, no.24, pp.17589-17609, 2021 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 33 Issue: 24
  • Publication Date: 2021
  • Doi Number: 10.1007/s00521-021-06344-5
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Applied Science & Technology Source, Biotechnology Research Abstracts, Compendex, Computer & Applied Sciences, Index Islamicus, INSPEC, zbMATH
  • Page Numbers: pp.17589-17609
  • Keywords: COVID-19, Computed tomography, Deep learning, Generative adversarial network, Lung segmentation, Data augmentation
  • Çanakkale Onsekiz Mart University Affiliated: Yes


COVID-19 has caused a pandemic crisis that threatens the world in many areas, especially in public health. For the diagnosis of COVID-19, computed tomography has a prognostic role in the early diagnosis of COVID-19 as it provides both rapid and accurate results. This is crucial to assist clinicians in making decisions for rapid isolation and appropriate patient treatment. Therefore, many researchers have shown that the accuracy of COVID-19 patient detection from chest CT images using various deep learning systems is extremely optimistic. Deep learning networks such as convolutional neural networks (CNNs) require substantial training data. One of the biggest problems for researchers is accessing a significant amount of training data. In this work, we combine methods such as segmentation, data augmentation and generative adversarial network (GAN) to increase the effectiveness of deep learning models. We propose a method that generates synthetic chest CT images using the GAN method from a limited number of CT images. We test the performance of experiments (with and without GAN) on internal and external dataset. When the CNN is trained on real images and synthetic images, a slight increase in accuracy and other results are observed in the internal dataset, but between 3% and 9% in the external dataset. It is promising according to the performance results that the proposed method will accelerate the detection of COVID-19 and lead to more robust systems.