Diagnosis of Covid-19 using fractional B-spline wavelet transform in lung ultrasound images

Document Type : Research Article

Authors

Department of Biomedical Engineering, College of Medical Science and Technologies, Tehran Science and Research Branch, Islamic Azad University, Tehran, Iran.

Abstract

The coronavirus spread rapidly in the world and caused the disease of Covid-19. The proposed research was conducted with the aim of classifying people with Covid-19 from other people. Lung Ultrasound (LUS) images were used to diagnose Covid-19. The open-source Point-Care-of-Ultrasound (POCUS) database was collected, which contained 59 LUS images and was collected from several other databases. In this research, a K-Nearest Neighbor (KNN) classifier with K-fold cross-validation was used to classify the feature matrix obtained from the Fractional B-Spline Wavelet Transform (FBSWT). The proposed method, Block-Matching and 3D filtering (BM3D) filter, was used in some methods, which had acceptable results. The proposed method was used to classify healthy people from patients with Covid-19. The results show that when the features based on the Wavelet Transform (WT) are used, the proposed method can achieve 90.90% sensitivity, 92.30% specificity, and 91.42% accuracy. While the features extracted from FBSWT show that the proposed method can achieve 95.45% sensitivity, 93.30% specificity, and 94.28% accuracy. Fractional transforms, especially the FBSWT, can be a useful tool for image processing. They can be used for various purposes such as detection and classification. By using FBSWT, it is possible to accurately diagnose the disease of Covid-19.

Keywords

Main Subjects

References

References
1. Yüce, M., Filiztekin, E., and Özkaya, K.G. “Covid-19 diagnosis -A review of current methods”, Biosens Bioelectron, 172, 112752 (2021). https://doi.org/10.1016/j.bios.2020.112752
2. Kashiwagi, K., Ishii Y., Aoki K., et al. “Immunochromatographic test for the detection of SARS-CoV-2 in saliva”, J. Infect Chemother, 27(2), pp. 384-386 (2021). https://doi.org/10.1016/j.jiac.2020.11.016
3. Sharma, A., Ahmad Farouk, I., and Lal, S.K. “Covid-19: A review on the novel coronavirus disease evolution, transmission, detection, control and prevention”, Viruses, 13(2), 202 (2021). https://doi.org/10.3390/v13020202
4. de Miguel-Yanes, J.M., Lopez-de-Andres, A., Jiménez-Garcia, R., et al. “Incidence, outcomes and sex-related disparities in pneumonia: A matched-pair analysis with data from Spanish hospitals (2016-2019)”, Journal of Clinical Medicine, 10(19), 4339 (2021). https://doi.org/10.3390/jcm10194339
5. Cunha, B.A. “Pneumonia in the elderly”, Clinical Microbiology and Infection, 7(11), pp. 581-588 (2001).
6. Picano, E., Scali, M.C., Ciampi, Q., et.al. “Lung ultrasound for the cardiologist”, JACC Cardiovasc Imaging, 11(11), pp. 1692-1705 (2018). https://doi.org/10.1016/j.jcmg.2018.06.023
7. Smith, M.J., Hayward, S.A., Innes, S.M., et al. “Point-of-care lung ultrasound in patients with Covid-19 - a narrative review”, Anaesthesia, 75(8), pp. 1096-1104 (2020). https://doi.org/10.1111/anae.15082
8. Hamet, P. and Tremblay, J. “Artificial intelligence in medicine”, Metabolism, 69S:S36-S40 (2017). https://doi.org/10.1016/j.metabol.2017.01.011
9. Kumar, V., Singh, D., Kaur, M., et al. “Overview of current state of research on the application of artificial intelligence techniques for Covid-19”, Peer J. Computer Science, 26(7), 564 (2021). https://doi.org/10.7717/peerj-cs.564
10. Zhang, D., Fundamentals of Image Data Mining: Analysis, Features, Classification and Retrieval, Springer International Publishing: Cham (2021). https://doi.org/10.1007/978-3-030-69251-3
11. Jin, W., Dai., L., Ge, L., et al. “Wavelet transform image enhancement algorithm-based evaluation of lung recruitment effect and nursing of acute respiratory distress syndrome by ultrasound image”, Journal of Healthcare Engineering, 8960465 (2021). https://doi.org/10.1155/2021/8960465
12. Mendlovic, D., Zalevsky, Z., Mas, D., et al. “Fractional wavelet transform”, Applied Optics, 36(20), pp. 4801-4806 (1997). https://doi.org/10.1364/AO.36.004801
13. Shi, J., Zhang, N., and Liu, X. “A novel fractional wavelet transforms and its applications”, Science China Information Sciences, 55(6), pp. 1270-1279 (2012). https://doi.org/10.1007/s11432-011-4320-x
14. Muhammad, G. and Hossain, M.Sh. “Covid-19 and non-Covid-19 classification using multi-layers fusion from lung ultrasound images”, Information Fusion, 72, pp. 80-88 (2021). https://doi.org/10.1016/j.inffus.2021.02.013
15. Born, J., Brandle, G., Cossio, M., et al. “Pocovid-Net: automatic detection of Covid-19 from a new lung ultrasound imaging dataset (POCUS)”, ArXiv Preprint ArXiv:200,4/12084 (2020). https://doi.org/10.48550/arXiv.2004.12084
16. Olgers, T.J., Azizi N., Blans, M.J., et al. “Point-of-care Ultrasound (PoCUS) for the internist in acute medicine: A uniform curriculum”, The Netherlands Journal of Medicine, 77(5), pp. 168-176 (2019).
17. Shi, J., Zhao Y., Xiang W., et al. “Deep scattering network with fractional wavelet transform”, IEEE Transactions on Signal Processing, 69, pp. 4740-4757 (2021). https://doi.org/10.1109/TSP.2021.3098936
18. Diaz-Escobar, J., Ordóñez-Guillén, N.E., Villarreal-Reyes, S., et al. “Deep-learning based detection of Covid-19 using lung ultrasound imagery”, PLOS One, 16(8), 0255886 (2021). https://doi.org/10.1371/journal.pone.0255886
19. Ebadi, S.E., Krishnaswamy, D., Seyed Bolouri, S.E., et al. “Automated detection of pneumonia in lung ultrasound using deep video classification for Covid-19”, Informatics in Medicine Unlocked. 25, 100687 (2021). https://doi.org/10.1016/j.imu.2021.100687
20. Baum, Z.M.C., Bonmati, E., Cristoni, L., et al. “Image quality assessment for closed-loop computer-assisted lung ultrasound”, SPIE Digital Library, 11598, pp. 183-189 (2021). https://doi.org/10.48550/arXiv.2008.08840
21. Awasthi, N., Dayal, A., Cenkeramaddi, L.R., et al. “Mini-COVIDNet: Efficient lightweight deep neural network for ultrasound-based point-of-care detection of Covid-19”, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 68(6), pp. 2023-2037 (2021). https://doi.org/10.1109/tuffc.2021.3068190
22. Born, J., Wiedemann., N., Cossio., M., et al. “Accelerating detection of lung pathologies with explainable ultrasound image analysis”, Applied Sciences, 11(2), 672 (2021). https://doi.org/10.3390/app11020672
23. Balocco, S., Gatta, C., Ferre, J.M., et al., Ultrasound Despeckle Methods, in Ultrasound Imaging: Advances and Applications, J.M. Sanches, A.F. Laine, and J.S. Suri, Editors, Springer US: Boston, MA., pp. 49-71 (2012). https://doi.org/10.1007/978-1-4614-1180-2_3
24. Djurović, I. “BM3D filter in salt-and-pepper noise removal”, EURASIP Journal on Image and Video Processing. 2016(1), 13 (2016). https://doi.org/10.1186/s13640-016-0113-x
25. Zhang, J., Xiong, R., Zhao, C., et al. “CONCOLOR: Constrained non-convex low-rank model for image deblocking”, IEEE Transactions on Image Processing, 25(3), pp. 1246-1259 (2016). https://doi.org/10.1109/TIP.2016.2515985
26. Yahya, A.A., Tan, J., Su, B., et al. “BM3D image denoising algorithm based on an adaptive filtering”, Multimedia Tools and Applications, 79, pp. 20391-20427 (2020). https://doi.org/10.1007/s11042-020-08815-8
27. Barki, Z., Seddik, C.B.J., and Seddik, H. “Building a modified block matching kernel based on Wave Atom transform for efficient image denoising”, The Egyptian Journal of Remote Sensing and Space Science, 24(3), pp. 857-878 (2021). https://doi.org/10.1016/j.ejrs.2021.08.009
28. Mostafa, A., Barghash, T.O., Assaf, A.A.S., et al. “Multi-sensor gait analysis for gender recognition”, In ICINCO (2020). https://doi.org/10.5220/0009792006290636
29. Blu, T. and Unser, M. “The fractional spline wavelet transforms: definition end implementation”, In IEEE International Conference on Acoustics, Speech, and Signal Processing, 1, pp. 512-515 (2000). https://doi.org/10.1109/ICASSP.2000.862030
30. Roberts, J. and Tsiligkaridis, T. “Ultrasound diagnosis of Covid-19: robustness and explain ability”, arXiv preprint arXiv:2012.01145 (2020). https://doi.org/10.48550/arXiv.2012.01145
31. Roy, S., Menapace, W., Oei, S., et al. “Deep learning for classification and localization of Covid-19 markers in point-of-care lung ultrasound”, IEEE Trans. Med. Imaging, 39(8), pp. 2676–2687 (2020). https://doi.org/10.1109/tmi.2020.2994459
32. Liu, L., Lei, W., Wan, X., et al. “Semi-supervised active learning for Covid-19 lung ultrasound multi-symptom classification”, In 2020 IEEE 32nd International Conference on Tools with Artificial Intelligence (ICTAI), pp. 1268-1273 (2020). https://doi.org/10.1109/ICTAI50040.2020.00191
33. Barros, B., Lacerda, P., Albuquerque, C., et al. “Pulmonary Covid-19: Learning spatiotemporal features combining CNN and LSTM networks for lung ultrasound video classification”, Sensors, 21(16), 5486 (2021). https://doi.org/10.3390/s21165486
34. Arntfield, R., VanBerlo, B., Alaifan, T., et al. “Development of a convolutional neural network to differentiate among the etiology of similar appearing pathological B lines on lung ultrasound: A deep learning study”, BMJ open., 11(3): e045120 (2021). https://doi.org/10.1136/bmjopen-2020-045120
Scientia Iranica
Volume 32, Issue 15
Transactions on Computer Science & Engineering and Electrical Engineering
July and August 2025 Article ID:7254

Files

History

  • Receive Date: 25 October 2022
  • Revise Date: 16 January 2023
  • Accept Date: 15 April 2023

Share

How to cite

Statistics