Acta Scientific Computer Sciences

Research Article Volume 4 Issue 4

DLCC: Deep Learning in Effective COVID-19 Classification

Parthajit Borah, Upasana Sarmah* and DK Bhattacharyya

Department of Computer Science and Engineering, Tezpur University, Napaam, Assam, India

*Corresponding Author: Upasana Sarmah, Department of Computer Science and Engineering, Tezpur University, Napaam, Assam, India.

Received: November 15, 2021; Published: March 17, 2022

Abstract

Recent history is not that generous and kind when it comes to viral infections from animal reservoirs to target humans. Re-emergence of mutating strains of such virus has only added more misery. In 2019, SARS-CoV2 had a daunting presence in and around the world, pausing grave threats to the perspective of global health, economy, livelihood and human race itself. Although numerous rigorous efforts are made to contain the contagious disease, there is a continuous steep rise in the number of clinically confirmed cases and fatalities. Medical facilities across the globe are in a crisis particularly when it comes to conducting adequate testing. RT-PCR (Reverse Transcription-Polymerase Chain Reaction) although reliable, consumes a lot of valuable time. Thus, an automated COVID-19 diagnosis strategy with efficient detection and minimum error is the need of the hour. Recently, Deep Learning techniques have become very popular for detecting COVID-19 using chest X-Ray images. In this paper, we propose a deep learning-based approach called DLCC to classify COVID-19 chest X-Rays with high accuracy. DLCC includes eight CNN architectures namely ResNet (18, 34, 50), AlexNet, VGG, and DenseNet (121, 161, 169) for best possible classification of diseased instances. All the models are fine-tuned using transfer learning. For the purpose of validation, a publicly available dataset containing four classes of chest radiograph, namely COVID-19, Lung opacity, Viral pneumonia and normal samples. From our study, it has been observed that DenseNet model gives the best performance in terms of accuracy (96.4%). This work might be used as a base to develop more effective CNN-based models for early detection of COVID-19.


Keywords: COVID-19; Pandemic; Virus; Genome; Strains; Coronavirus

References

  1. Alakus TB and Turkoglu I. “Comparison of deep learning approaches to predict COVID-19 infection”. Chaos, Solitons and Fractals 140 (2020): 110120.
  2. Chowdhury ME., et al. “Can AI help in screening viral and COVID-19 pneumonia?” IEEE Access 8 (2020): 132665-132676.
  3. Cohen JP., et al. “COVID-19 Image Data Collection: Prospective Predictions Are the Future” (2020).
  4. Daniel K., et al. “Labeled Optical Coherence Tomography (OCT) and Chest X-Ray Images for Classification” (2018).
  5. Diaz-Escobar J., et al. “Deep-learning based detection of COVID-19 using lung ultrasound imagery”. Plos One 16 (2021): e0255886.
  6. Haghanifar A., et al. “COVID-19 Chest X-Ray Image Repository figshare Dataset” (2020).
  7. Ibrahim DM., et al. “Deep-chest: Multi-classification deep learning model for diagnosing COVID-19, pneumonia, and lung cancer chest diseases”. Computers in Biology and Medicine 132 (2021): 104348.
  8. Ismael AM., et al. “Deep Learning Approaches for COVID-19 Detection based on Chest X-ray images”. Expert Systems with Applications 164 (2021): 114054.
  9. Kingma DP and Ba J. “Adam: A method for stochastic optimization”. arXiv preprint arXiv:1412.6980 (2014).
  10. MdlI V., et al. “Bimcv covid-19+: a large annotated dataset of rx and ct images from covid-19 patients” (2020).
  11. Ostuni JL., et al. “Analysis of Interpolation Effects in the Reslicing of Functional MR Images”. Journal of Computer Assisted Tomography 21 (1997): 803-810.
  12. Rahman T., et al. “Exploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images”. Computers in Biology and Medicine 132 (2021): 104319.
  13. Rohila VS., et al. “Deep learning assisted covid-19 detection using full ct-scans”. Internet of Things 14 (2021): 100377.
  14. Selvaraju RR., et al. “Grad-cam: Why did you say that?” arXiv preprint arXiv:1611.07450 (2016).
  15. Shorten C and Khoshgoftaar TM. “A Survey on Image Data Augmentation for Deep Learning”. Journal of Big Data 6 (2019): 1-48.
  16. Sohrabi C., et al. “World Health Organization Declares Global Emergency: A Review of the 2019 Novel Coronavirus (COVID-19)”. International Journal of Surgery (2020).
  17. Torrey L., et al. “Transfer learning, in: Handbook of research on machine learning applications and trends: algorithms, methods, and techniques”. IGI Global (2010): 242-264.
  18. Wang J., et al. “The Effectiveness of Data Augmentation in Image Classification using Deep Learning”. Convolutional Neural Networks Vis. Recognit 11 (2017): 1-8.
  19. Winther HB., et al. “COVID-19 Image Repository” (2020).
  20. Xu Y., et al. “Manet: A two-stage deep learning method for classification of covid-19 from chest x-ray im- ages”. Neurocomputing 443 (2021): 96-105.

Citation

Citation: Parthajit Borah., et al. “DLCC: Deep Learning in Effective COVID-19 Classification". Acta Scientific Computer Sciences 4.4 (2022): 41-47.

Copyright

Copyright: © 2022 Parthajit Borah., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.




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