International Journal of Computer Science & Network Security

International Journal of Computer Science & Network Security (국제컴퓨터통신보호논문지학회)
권호 표지
DOI QR코드

DOI QR Code

COVID-19: Improving the accuracy using data augmentation and pre-trained DCNN Models

  • Saif Hassan (Center of Excellence for Robotics, Artificial Intelligence, and Blockchain, Department of Computer Science, Sukkur IBA University) ;
  • Abdul Ghafoor (Center of Excellence for Robotics, Artificial Intelligence, and Blockchain, Department of Computer Science, Sukkur IBA University) ;
  • Zahid Hussain Khand (Center of Excellence for Robotics, Artificial Intelligence, and Blockchain, Department of Computer Science, Sukkur IBA University) ;
  • Zafar Ali (Center of Excellence for Robotics, Artificial Intelligence, and Blockchain, Department of Computer Science, Sukkur IBA University) ;
  • Ghulam Mujtaba (Center of Excellence for Robotics, Artificial Intelligence, and Blockchain, Department of Computer Science, Sukkur IBA University) ;
  • Sajid Khan (Center of Excellence for Robotics, Artificial Intelligence, and Blockchain, Department of Computer Science, Sukkur IBA University)
  • Received : 2024.07.05
  • Published : 2024.07.30
Download PDF
⟨ Previous Next ⟩

Abstract

Since the World Health Organization (WHO) has declared COVID-19 as pandemic, many researchers have started working on developing vaccine and developing AI systems to detect COVID-19 patient using Chest X-ray images. The purpose of this work is to improve the performance of pre-trained Deep convolution neural nets (DCNNs) on Chest X-ray images dataset specially COVID-19 which is developed by collecting from different sources such as GitHub, Kaggle. To improve the performance of Deep CNNs, data augmentation is used in this study. The COVID-19 dataset collected from GitHub was containing 257 images while the other two classes normal and pneumonia were having more than 500 images each class. There were two issues whike training DCNN model on this dataset, one is unbalanced and second is the data is very less. In order to handle these both issues, we performed data augmentation such as rotation, flipping to increase and balance the dataset. After data augmentation each class contains 510 images. Results show that augmentation on Chest X-ray images helps in improving accuracy. The accuracy before and after augmentation produced by our proposed architecture is 96.8% and 98.4% respectively.

Keywords

References

  1. https://www.who.int/health-topics/coronavirus 
  2. https://www.who.int/docs/defaultsource/coronaviruse/situation-reports/20200718-covid-19-sitrep-180.pdf?sfvrsn=39b31718_2 
  3. https://www.who.int/emergencies/diseases/novel-coronavirus-2019/technical-guidance/naming-the-coronavirus-disease-(covid-2019)-and-the-virus-that-causes-it 
  4. Fernandes, Nuno. "Economic effects of coronavirus outbreak (COVID-19) on the world economy." Available at SSRN 3557504 (2020). 
  5. https://www.statista.com/statistics/1106302/coronavirus-impact-index-by-industry-2020/ 
  6. https://www.cdc.gov/coronavirus/2019-ncov/symptoms-testing/symptoms.html
  7. https://www.who.int/emergencies/diseases/novel-coronavirus-2019/question-and-answers-hub/q-a-detail/q-a-coronaviruses#:~:text=protect 
  8. Wang, Linda, and Alexander Wong. "COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Images." arXiv preprint arXiv:2003.09871 (2020). 
  9. Wang, Wenling, et al. "Detection of SARS-CoV-2 in different types of clinical specimens." Jama 323.18 (2020): 1843-1844. 
  10. Huang, Chaolin, et al. "Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China." The lancet 395.10223 (2020): 497-506. 
  11. Ng, Ming-Yen, et al. "Imaging profile of the COVID-19 infection: radiologic findings and literature review." Radiology: Cardiothoracic Imaging 2.1 (2020): e200034. 
  12. Guan, Wei-jie, et al. "Clinical characteristics of coronavirus disease 2019 in China." New England journal of medicine 382.18 (2020): 1708-1720. 
  13. Ai, Tao, et al. "Correlation of chest CT and RT-PCR testing in coronavirus disease 2019 (COVID-19) in China: a report of 1014 cases." Radiology (2020): 200642. 
  14. Rubin, Geoffrey D., et al. "The role of chest imaging in patient management during the COVID-19 pandemic: a multinational consensus statement from the Fleischner Society." Chest (2020). 
  15. Nair, A., et al. "A British Society of Thoracic Imaging statement: considerations in designing local imaging diagnostic algorithms for the COVID-19 pandemic." Clinical Radiology 75.5 (2020): 329-334. 
  16. Adam Jacobi, A. B., Michael Chung & Eber, C. Portable chest x-ray in coronavirus disease-19 (covid 19): A pictorial review. Clin. Imaging (2020). 
  17. Maghdid, Halgurd S., et al. "Diagnosing COVID-19 pneumonia from X-ray and CT images using deep learning and transfer learning algorithms." arXiv preprint arXiv:2004.00038 (2020). 
  18. Wang, Shuai, et al. "A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19)." MedRxiv (2020). 
  19. Apostolopoulos, Ioannis D., and Tzani A. Mpesiana. "Covid-19: automatic detection from x-ray images utilizing transfer learning with convolutional neural networks." Physical and Engineering Sciences in Medicine (2020): 1. 
  20. Sethy, Prabira Kumar, and Santi Kumari Behera. "Detection of coronavirus disease (covid-19) based on deep features." Preprints 2020030300 (2020): 2020. 
  21. https://github.com/ieee8023/covid-chestxray-dataset 
  22. Madani, Ali, et al. "Chest x-ray generation and data augmentation for cardiovascular abnormality classification." Medical Imaging 2018: Image Processing. Vol. 10574. International Society for Optics and Photonics, 2018. 
  23. Shorten, Connor, and Taghi M. Khoshgoftaar. "A survey on image data augmentation for deep learning." Journal of Big Data 6.1 (2019): 60