Image Pre-processing techniques comparison: COVID-19 detection through Chest X-Rays via Deep Learning

Authors

  • Arunit Maity  School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
  • Tusshaar R Nair  School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
  • Avinash Chandra  School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

DOI:

https://doi.org/10.32628/IJSRST207614

Keywords:

Radiography, Chest X-Rays (CXRs), COVID-19, Histogram Equalization (HE), Adaptive Histogram Equalization (AHE), Contrast Limited Adaptive Histogram Equalization (CLAHE), Top hat Bottom Hat Transform, image pre-processing, Convolutional Neural Networks (CNNs)

Abstract

The COVID-19 pandemic has had a very devastating effect and has spread rapidly across the world affecting close to 36 million people. Chest radiography is a very important feature which is used for early diagnosis of various diseases. With the increasing pandemic, there is a growing popularity of training Convolutional Neural Networks (CNN) to diagnose and detect COVID-19 from Chest X-Rays. However, publicly available and medically verified datasets for COVID-19 infected chest X-Rays are scarce, which results in the model not generalizing properly. For this purpose, it is important to pre-process and augment the data being used to train the model. Various pre-processing techniques exist like Global Histogram Equalization (GHE), Contrast Limited Adaptive Histogram Equalization (CLAHE) and Top Bottom Hat Transform. In this review, we study and compare all these pre-processing techniques to understand which is the most suitable for developing a CNN model which can classify an image as being infected with COVID-19 or Viral Pneumonia with high efficacy.

References

  1. Wu F., Zhao S., Yu B. A new coronavirus associated with human respiratory disease in China. Nature. 2020;579(7798):265–269.
  2. Huang C., Wang Y. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet. 2020;395(10223):497–506.
  3. World Health Organization. World Health Organization (WHO); 2020. Pneumonia of Unknown Cause–China. Emergencies Preparedness, Response, Disease Outbreak News.
  4. Rubin, G. D. et al. The role of chest imaging in patient management during the COVID-19 pandemic: A multinational consensus statement from the fleischner society. Radiology (2020).
  5. Adam J., A. B., Michael C.& Eber, C. Portable chest X-ray in coronavirus disease-19 (COVID-19): A pictorial review. Clin. Imaging (2020).
  6. Wong, H. et al. Frequency and distribution of chest radiographic findings in COVID-19 positive patients. Radiology (2020).
  7. Luca B., FrancescoM., AlfonsoR., AntonellaS. Explainable Deep Learning for Pulmonary Disease and Coronavirus COVID-19 Detection from X-rays. Computer Methods and Programs in Biomedicine Volume 196, November 2020, 105608
  8. Mohammad K. P., Shoaib A. B. SARS n-CoV2-19 detection from chest x-ray images using deep neural net. International Journal of Pervasive Computing and CommunicationsVolume 16 Issue 5
  9. Ozturk T, Talo M, Yildirim EA, Baloglu UB, Yildirim O, Rajendra Acharya U. Automated detection of COVID-19 cases using deep neural networks with X-ray images. ComputBiol Med. 2020 Jun
  10. Yu P, Xu H, Zhu Y, Yang C, Sun X, Zhao J (2011) An automatic computer-aided detection scheme for pneumoconiosis on digital chest radiographs. J Digit Imaging 24(3):382–393 11.
  11. Caobelli F. Artificial intelligence in medical imaging: Game over for radiologists? Eur J Radiol. 2020 May
  12. Ai, T. et al. Correlation of Chest CT and RT-PCR Testing in Coronavirus Disease 2019 (COVID-19) in China: A Report of 1014 Cases. Radiology 200642 (2020)
  13. Pan, S. J. & Yang, Q. A survey on transfer learning. IEEE Transactions on knowledge data engineering 22, 1345–1359 (2009).
  14. T. Carvalho, E. R. S. de Rezende, M. T. P. Alves, F. K. C. Balieiro and R. B. Sovat, "Exposing Computer Generated Images by Eye’s Region Classification via Transfer Learning of VGG19 CNN," 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA), Cancun, 2017, pp. 866-870.
  15. W. Zhihong and X. Xiaohong, "Study on Histogram Equalization," 2011 2nd International Symposium on Intelligence Information Processing and Trusted Computing, Hubei, 2011, pp. 177-179
  16. Stephen M. Pizer, E. Philip A., John D. A., Robert C., Ari G., Trey G., Bart ter H. R., John B. Z., Karel Z.,Adaptive histogram equalization and its variations,Computer Vision, Graphics, and Image Processing,Volume 39, Issue 3,1987,Pages 355-368
  17. Georgieva V., Kountchev R., Draganov I. (2013) An Adaptive Enhancement of X-Ray Images. In: Kountchev R., Iantovics B. (eds) Advances in Intelligent Analysis of Medical Data and Decision Support Systems. Studies in Computational Intelligence, vol 473. Springer, Heidelberg
  18. Reza, A.M. Realization of the Contrast Limited Adaptive Histogram Equalization (CLAHE) for Real-Time Image Enhancement. The Journal of VLSI Signal Processing-Systems for Signal, Image, and Video Technology 38, 35–44 (2004)
  19. Kushol, Rafsanjany& Rahman, A. B. M. Ashikur& Salekin, Md Sirajus& Raihan, Md. Nishat. (2018). Contrast Enhancement of Medical X-Ray Image Using Morphological Operators with Optimal Structuring Element. https://arxiv.org/pdf/1905.08545.pdf\
  20. I. Sirazitdinov, M. Kholiavchenko, R. Kuleev and B. Ibragimov, "Data Augmentation for Chest Pathologies Classification," 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), Venice, Italy, 2019, pp. 1216-1219
  21. L. Perez and J. Wang. The effectiveness of data augmentation in image classification using deep learning. arXiv preprint arXiv:1712.04621, 2017
  22. Karen S., Andrew Z. Very Deep Convolutional Networks for Large-Scale Image RecognitionarXiv:1409.1556
  23. Tammina, Srikanth. (2019). Transfer learning using VGG-16 with Deep Convolutional Neural Network for Classifying Images. International Journal of Scientific and Research Publications (IJSRP).
  24. L. Wen, L. Gao and X. Li, "A New Snapshot Ensemble Convolutional Neural Network for Fault Diagnosis," in IEEE Access, vol. 7, pp. 32037-32047, 2019
  25. Hossin M., Sulaiman M. N., A review on evaluation metrics for data classification evaluationsInt. J. Data Min. Knowl. Manage. Process, 5 (2) (2015), p. 1
  26. M.E.H. Chowdhury, T. Rahman, A. Khandakar, R. Mazhar, M.A. Kadir, Z.B. Mahbub, K.R. Islam, M.S. Khan, A. Iqbal, N. Al-Emadi, M.B.I. Reaz, M. T. Islam, “Can AI help in screening Viral and COVID-19 pneumonia?” IEEE Access, Vol. 8, 2020, pp. 132665 - 132676

International Journal of Scientific Research in Science and Technology

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Published

2020-12-30

Issue

Volume 7, Issue 6, November-December-2020

Section

Research Articles

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How to Cite

[1]
Arunit Maity, Tusshaar R Nair, Avinash Chandra "Image Pre-processing techniques comparison: COVID-19 detection through Chest X-Rays via Deep Learning " International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011,Volume 7, Issue 6, pp.113-123, November-December-2020. Available at doi : https://doi.org/10.32628/IJSRST207614