Research Article
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Rapid and Precise Identification of COVID-19 through Segmentation and Classification of CT and X-ray Images

Year 2023, Volume: 6 Issue: 2, 123 - 139, 31.08.2023

Ahmet Saygılı

https://doi.org/10.35377/saucis...1309970

Abstract

The COVID-19 pandemic, caused by a novel coronavirus, has become a global epidemic. Although the reverse transcription-polymerase chain reaction (RT-PCR) test is the current gold standard for detecting the virus, its low reliability has led to the use of CT and X-ray imaging in diagnostics. As limited vaccine availability necessitates rapid and accurate detection, this study applies k-means and fuzzy c-means segmentation to CT and X-ray images to classify COVID-19 cases as either diseased or healthy for CT scans and diseased, healthy, or non-COVID pneumonia for X-rays. Our research employs four open-access, widely-used datasets and is conducted in four stages: preprocessing, segmentation, feature extraction, and classification. During feature extraction, we employ the Gray-Level Co-Occurrence Matrix (GLCM), Local Binary Pattern (LBP), and Histogram of Oriented Gradients (HOG). In the classification process, our approach involves utilizing k-Nearest Neighbor (kNN), Support Vector Machines (SVM), and Extreme Learning Machines (ELM) techniques. Our research achieved a sensitivity rate exceeding 99%, which is higher than the 60-70% sensitivity rate of PCR tests. As a result, our study can serve as a decision support system that can help medical professionals make rapid and precise diagnoses with a high level of sensitivity.

Keywords

COVID-19 Diagnosis Imaging techniques Segmentation methods Machine learning-based classification

Supporting Institution

Research Projects Coordination Unit of Tekirdağ Namık Kemal University

Project Number

NKUBAP.06.GA.21.317

Thanks

This study was funded by the Scientific Research Projects Coordination Unit of Tekirdağ Namık Kemal University. Project number: NKUBAP.06.GA.21.317

References

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  • [3] C. M. A. de O. Lima, “Information about the new coronavirus disease (COVID-19),” Radiol Bras, vol. 53, no. 2, pp. V–VI, 2020.
  • [4] M. W. M. Mustafa, “Audiological profile of asymptomatic Covid-19 PCR-positive cases,” Am J Otolaryngol, vol. 41, no. 3, p. 102483, 2020.
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  • [6] L. Lan et al., “Positive RT-PCR test results in patients recovered from COVID-19,” JAMA, vol. 323, no. 15, pp. 1502–1503, 2020.
  • [7] T. Ai et al., “Correlation of chest CT and RT-PCR testing for coronavirus disease 2019 (COVID-19) in China: a report of 1014 cases,” Radiology, vol. 296, no. 2, pp. E32–E40, 2020.
  • [8] H. Shi et al., “Radiological findings from 81 patients with COVID-19 pneumonia in Wuhan, China: a descriptive study,” Lancet Infect Dis, vol. 20, no. 4, pp. 425–434, 2020.
  • [9] D. Dong et al., “The role of imaging in the detection and management of COVID-19: a review,” IEEE Rev Biomed Eng, 2020.
  • [10] C. Hani et al., “COVID-19 pneumonia: a review of typical CT findings and differential diagnosis,” Diagn Interv Imaging, vol. 101, no. 5, pp. 263–268, 2020.
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  • [24] N. K. Mishra, P. Singh, and S. D. Joshi, “Automated Detection of COVID-19 from CT scan using Convolutional Neural Network,” Biocybern Biomed Eng, 2021.
  • [25] S. Chakraborty and K. Mali, “SuFMoFPA: A superpixel and meta-heuristic based fuzzy image segmentation approach to explicate COVID-19 radiological images,” Expert Syst Appl, vol. 167, p. 114142, 2021.
  • [26] A. A. Ardakani, A. R. Kanafi, U. R. Acharya, N. Khadem, and A. Mohammadi, “Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks,” Comput Biol Med, vol. 121, p. 103795, 2020.
  • [27] G. Gilanie et al., “Coronavirus (COVID-19) detection from chest radiology images using convolutional neural networks,” Biomed Signal Process Control, vol. 66, p. 102490, 2021.
  • [28] P. Kalane, S. Patil, B. P. Patil, and D. P. Sharma, “Automatic detection of COVID-19 disease using U-Net architecture based fully convolutional network,” Biomed Signal Process Control, vol. 67, p. 102518, 2021.
  • [29] Y. Li et al., “Efficient and effective training of COVID-19 classification networks with self-supervised dual-track learning to rank,” IEEE J Biomed Health Inform, vol. 24, no. 10, pp. 2787–2797, 2020.
  • [30] X. Xu et al., “A deep learning system to screen novel coronavirus disease 2019 pneumonia,” Engineering, vol. 6, no. 10, pp. 1122–1129, 2020.
  • [31] A. Jaiswal, N. Gianchandani, D. Singh, V. Kumar, and M. Kaur, “Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning,” J Biomol Struct Dyn, pp. 1–8, 2020.
  • [32] N. D. Kathamuthu et al., “A deep transfer learning-based convolution neural network model for COVID-19 detection using computed tomography scan images for medical applications,” Advances in Engineering Software, vol. 175, p. 103317, Jan. 2023, doi: 10.1016/j.advengsoft.2022.103317.
  • [33] V. Göreke, V. Sarı, and S. Kockanat, “A novel classifier architecture based on deep neural network for COVID-19 detection using laboratory findings,” Appl Soft Comput, vol. 106, p. 107329, 2021.
  • [34] J. Zhao, Y. Zhang, X. He, and P. Xie, “Covid-ct-dataset: a ct scan dataset about covid-19,” arXiv preprint arXiv:2003.13865, vol. 490, 2020.
  • [35] P. Angelov and E. Almeida Soares, “SARS-CoV-2 CT-scan dataset: A large dataset of real patients CT scans for SARS-CoV-2 identification,” medRxiv, 2020.
  • [36] J. P. Cohen, P. Morrison, L. Dao, K. Roth, T. Q. Duong, and M. Ghassemi, “Covid-19 image data collection: Prospective predictions are the future,” arXiv preprint arXiv:2006.11988, 2020.
  • [37] X. Wang, Y. Peng, L. Lu, Z. Lu, M. Bagheri, and R. M. Summers, “Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 2097–2106.
  • [38] M. E. H. Chowdhury et al., “Can AI help in screening viral and COVID-19 pneumonia?,” IEEE Access, vol. 8, pp. 132665–132676, 2020.
  • [39] T. Rahman et al., “Exploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images,” Comput Biol Med, vol. 132, p. 104319, 2021.
  • [40] J. S. Lim and A. V Oppenheim, “Enhancement and bandwidth compression of noisy speech,” Proceedings of the IEEE, vol. 67, no. 12, pp. 1586–1604, 1979.
  • [41] R. M. Haralick and L. G. Shapiro, “Image segmentation techniques,” Comput Vis Graph Image Process, vol. 29, no. 1, pp. 100–132, 1985.
  • [42] J. C. Bezdek, R. Ehrlich, and W. Full, “FCM: The fuzzy c-means clustering algorithm,” Comput Geosci, vol. 10, no. 2–3, pp. 191–203, 1984.
  • [43] A. Likas, N. Vlassis, and J. J. Verbeek, “The global k-means clustering algorithm,” Pattern Recognit, vol. 36, no. 2, pp. 451–461, 2003.
  • [44] I. Davidson, “Understanding K-means non-hierarchical clustering,” SUNY Albany Technical Report, vol. 2, pp. 2–14, 2002.
  • [45] T. Pang-Ning, M. Steinbach, and V. Kumar, “Introduction to data mining Addison-Wesley,” 2005.
  • [46] P. K. Bhagat, P. Choudhary, and K. M. Singh, “Chapter 13 - A comparative study for brain tumor detection in MRI images using texture features,” in Sensors for Health Monitoring, N. Dey, J. Chaki, and R. Kumar, Eds., Academic Press, 2019, pp. 259–287. doi: https://doi.org/10.1016/B978-0-12-819361-7.00013-0.
  • [47] R. M. Haralick, K. Shanmugam, and I. H. Dinstein, “Textural features for image classification,” IEEE Trans Syst Man Cybern, no. 6, pp. 610–621, 1973.
  • [48] N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” in 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR’05), Ieee, 2005, pp. 886–893.
  • [49] T. Ojala, M. Pietikainen, and D. Harwood, “Performance evaluation of texture measures with classification based on Kullback discrimination of distributions,” in Proceedings of 12th international conference on pattern recognition, IEEE, 1994, pp. 582–585.
  • [50] T. Ojala, M. Pietikäinen, and D. Harwood, “A comparative study of texture measures with classification based on featured distributions,” Pattern Recognit, vol. 29, no. 1, pp. 51–59, 1996.
  • [51] G.-B. Huang, H. Zhou, X. Ding, and R. Zhang, “Extreme learning machine for regression and multiclass classification,” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 42, no. 2, pp. 513–529, 2011.
  • [52] G.-B. Huang, Q.-Y. Zhu, and C.-K. Siew, “Extreme learning machine: theory and applications,” Neurocomputing, vol. 70, no. 1–3, pp. 489–501, 2006.
  • [53] G.-B. Huang, Q.-Y. Zhu, and C.-K. Siew, “Extreme learning machine: a new learning scheme of feedforward neural networks,” in 2004 IEEE international joint conference on neural networks (IEEE Cat. No. 04CH37541), Ieee, 2004, pp. 985–990.
  • [54] V. Vapnik, The nature of statistical learning theory. Springer science & business media, 2013.
  • [55] F. H. Garabaghi, R. Benzer, S. Benzer, and A. Ç. Günal, “Effect of polynomial, radial basis, and Pearson VII function kernels in support vector machine algorithm for classification of crayfish,” Ecol Inform, vol. 72, p. 101911, Dec. 2022, doi: 10.1016/J.ECOINF.2022.101911.
  • [56] S. Arya, D. M. Mount, N. S. Netanyahu, R. Silverman, and A. Y. Wu, “An optimal algorithm for approximate nearest neighbor searching fixed dimensions,” Journal of the ACM (JACM), vol. 45, no. 6, pp. 891–923, 1998.
  • [57] A.-M. Šimundić, “Measures of diagnostic accuracy: basic definitions,” EJIFCC, vol. 19, no. 4, p. 203, 2009.

Year 2023, Volume: 6 Issue: 2, 123 - 139, 31.08.2023

Ahmet Saygılı

https://doi.org/10.35377/saucis...1309970

Abstract

Project Number

NKUBAP.06.GA.21.317

References

  • [1] “World Health Organization Coronavirus (COVID-19) Dashboard, Retrieved 05th May 2021. (Web Link: https://covid19.who.int/).” https://covid19.who.int/
  • [2] C. Wang et al., “Immediate psychological responses and associated factors during the initial stage of the 2019 coronavirus disease (COVID-19) epidemic among the general population in China,” Int J Environ Res Public Health, vol. 17, no. 5, p. 1729, 2020.
  • [3] C. M. A. de O. Lima, “Information about the new coronavirus disease (COVID-19),” Radiol Bras, vol. 53, no. 2, pp. V–VI, 2020.
  • [4] M. W. M. Mustafa, “Audiological profile of asymptomatic Covid-19 PCR-positive cases,” Am J Otolaryngol, vol. 41, no. 3, p. 102483, 2020.
  • [5] Y. Fang et al., “Sensitivity of chest CT for COVID-19: comparison to RT-PCR,” Radiology, vol. 296, no. 2, pp. E115–E117, 2020.
  • [6] L. Lan et al., “Positive RT-PCR test results in patients recovered from COVID-19,” JAMA, vol. 323, no. 15, pp. 1502–1503, 2020.
  • [7] T. Ai et al., “Correlation of chest CT and RT-PCR testing for coronavirus disease 2019 (COVID-19) in China: a report of 1014 cases,” Radiology, vol. 296, no. 2, pp. E32–E40, 2020.
  • [8] H. Shi et al., “Radiological findings from 81 patients with COVID-19 pneumonia in Wuhan, China: a descriptive study,” Lancet Infect Dis, vol. 20, no. 4, pp. 425–434, 2020.
  • [9] D. Dong et al., “The role of imaging in the detection and management of COVID-19: a review,” IEEE Rev Biomed Eng, 2020.
  • [10] C. Hani et al., “COVID-19 pneumonia: a review of typical CT findings and differential diagnosis,” Diagn Interv Imaging, vol. 101, no. 5, pp. 263–268, 2020.
  • [11] A. SAYGILI, “Analysis and Segmentation of X-ray Images of COVID-19 Patients using the k-means Algorithm,” Veri Bilimi, vol. 4, no. 3, pp. 1–6.
  • [12] A. Saygılı, “A new approach for computer-aided detection of coronavirus (COVID-19) from CT and X-ray images using machine learning methods,” Appl Soft Comput, vol. 105, p. 107323, 2021.
  • [13] A. Saygılı, “Computer-aided detection of COVID-19 from CT images based on Gaussian mixture model and kernel support vector machines classifier,” Arab J Sci Eng, vol. 47, no. 2, pp. 2435–2453, 2022.
  • [14] B. Abraham and M. S. Nair, “Computer-aided detection of COVID-19 from X-ray images using multi-CNN and Bayesnet classifier,” Biocybern Biomed Eng, vol. 40, no. 4, pp. 1436–1445, 2020.
  • [15] R. C. Joshi et al., “A deep learning-based COVID-19 automatic diagnostic framework using chest X-ray images,” Biocybern Biomed Eng, vol. 41, no. 1, pp. 239–254, 2021.
  • [16] M. F. Aslan, M. F. Unlersen, K. Sabanci, and A. Durdu, “CNN-based transfer learning–BiLSTM network: A novel approach for COVID-19 infection detection,” Appl Soft Comput, vol. 98, p. 106912, 2021, doi: https://doi.org/10.1016/j.asoc.2020.106912.
  • [17] F. Demir, “DeepCoroNet: A deep LSTM approach for automated detection of COVID-19 cases from chest X-ray images,” Appl Soft Comput, vol. 103, p. 107160, 2021.
  • [18] T. Ozturk, M. Talo, E. A. Yildirim, U. B. Baloglu, O. Yildirim, and U. R. Acharya, “Automated detection of COVID-19 cases using deep neural networks with X-ray images,” Comput Biol Med, vol. 121, p. 103792, 2020.
  • [19] A. Altan and S. Karasu, “Recognition of COVID-19 disease from X-ray images by hybrid model consisting of 2D curvelet transform, chaotic salp swarm algorithm and deep learning technique,” Chaos Solitons Fractals, vol. 140, p. 110071, 2020.
  • [20] M. Nour, Z. Cömert, and K. Polat, “A novel medical diagnosis model for COVID-19 infection detection based on deep features and Bayesian optimization,” Appl Soft Comput, vol. 97, p. 106580, 2020.
  • [21] A. Gupta, S. Gupta, and R. Katarya, “InstaCovNet-19: A deep learning classification model for the detection of COVID-19 patients using Chest X-ray,” Appl Soft Comput, vol. 99, p. 106859, 2021.
  • [22] J. Sikder, N. Datta, and D. Tripura, “A Deep Learning Approach for Recognizing Covid-19 from Chest X-ray using Modified CNN-BiLSTM with M-SVM,” in 2022 International Conference on Electrical, Computer and Energy Technologies (ICECET), IEEE, Jul. 2022, pp. 1–6. doi: 10.1109/ICECET55527.2022.9872776.
  • [23] L. Kong and J. Cheng, “Classification and detection of COVID-19 X-Ray images based on DenseNet and VGG16 feature fusion,” Biomed Signal Process Control, vol. 77, p. 103772, Aug. 2022, doi: 10.1016/j.bspc.2022.103772.
  • [24] N. K. Mishra, P. Singh, and S. D. Joshi, “Automated Detection of COVID-19 from CT scan using Convolutional Neural Network,” Biocybern Biomed Eng, 2021.
  • [25] S. Chakraborty and K. Mali, “SuFMoFPA: A superpixel and meta-heuristic based fuzzy image segmentation approach to explicate COVID-19 radiological images,” Expert Syst Appl, vol. 167, p. 114142, 2021.
  • [26] A. A. Ardakani, A. R. Kanafi, U. R. Acharya, N. Khadem, and A. Mohammadi, “Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks,” Comput Biol Med, vol. 121, p. 103795, 2020.
  • [27] G. Gilanie et al., “Coronavirus (COVID-19) detection from chest radiology images using convolutional neural networks,” Biomed Signal Process Control, vol. 66, p. 102490, 2021.
  • [28] P. Kalane, S. Patil, B. P. Patil, and D. P. Sharma, “Automatic detection of COVID-19 disease using U-Net architecture based fully convolutional network,” Biomed Signal Process Control, vol. 67, p. 102518, 2021.
  • [29] Y. Li et al., “Efficient and effective training of COVID-19 classification networks with self-supervised dual-track learning to rank,” IEEE J Biomed Health Inform, vol. 24, no. 10, pp. 2787–2797, 2020.
  • [30] X. Xu et al., “A deep learning system to screen novel coronavirus disease 2019 pneumonia,” Engineering, vol. 6, no. 10, pp. 1122–1129, 2020.
  • [31] A. Jaiswal, N. Gianchandani, D. Singh, V. Kumar, and M. Kaur, “Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning,” J Biomol Struct Dyn, pp. 1–8, 2020.
  • [32] N. D. Kathamuthu et al., “A deep transfer learning-based convolution neural network model for COVID-19 detection using computed tomography scan images for medical applications,” Advances in Engineering Software, vol. 175, p. 103317, Jan. 2023, doi: 10.1016/j.advengsoft.2022.103317.
  • [33] V. Göreke, V. Sarı, and S. Kockanat, “A novel classifier architecture based on deep neural network for COVID-19 detection using laboratory findings,” Appl Soft Comput, vol. 106, p. 107329, 2021.
  • [34] J. Zhao, Y. Zhang, X. He, and P. Xie, “Covid-ct-dataset: a ct scan dataset about covid-19,” arXiv preprint arXiv:2003.13865, vol. 490, 2020.
  • [35] P. Angelov and E. Almeida Soares, “SARS-CoV-2 CT-scan dataset: A large dataset of real patients CT scans for SARS-CoV-2 identification,” medRxiv, 2020.
  • [36] J. P. Cohen, P. Morrison, L. Dao, K. Roth, T. Q. Duong, and M. Ghassemi, “Covid-19 image data collection: Prospective predictions are the future,” arXiv preprint arXiv:2006.11988, 2020.
  • [37] X. Wang, Y. Peng, L. Lu, Z. Lu, M. Bagheri, and R. M. Summers, “Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 2097–2106.
  • [38] M. E. H. Chowdhury et al., “Can AI help in screening viral and COVID-19 pneumonia?,” IEEE Access, vol. 8, pp. 132665–132676, 2020.
  • [39] T. Rahman et al., “Exploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images,” Comput Biol Med, vol. 132, p. 104319, 2021.
  • [40] J. S. Lim and A. V Oppenheim, “Enhancement and bandwidth compression of noisy speech,” Proceedings of the IEEE, vol. 67, no. 12, pp. 1586–1604, 1979.
  • [41] R. M. Haralick and L. G. Shapiro, “Image segmentation techniques,” Comput Vis Graph Image Process, vol. 29, no. 1, pp. 100–132, 1985.
  • [42] J. C. Bezdek, R. Ehrlich, and W. Full, “FCM: The fuzzy c-means clustering algorithm,” Comput Geosci, vol. 10, no. 2–3, pp. 191–203, 1984.
  • [43] A. Likas, N. Vlassis, and J. J. Verbeek, “The global k-means clustering algorithm,” Pattern Recognit, vol. 36, no. 2, pp. 451–461, 2003.
  • [44] I. Davidson, “Understanding K-means non-hierarchical clustering,” SUNY Albany Technical Report, vol. 2, pp. 2–14, 2002.
  • [45] T. Pang-Ning, M. Steinbach, and V. Kumar, “Introduction to data mining Addison-Wesley,” 2005.
  • [46] P. K. Bhagat, P. Choudhary, and K. M. Singh, “Chapter 13 - A comparative study for brain tumor detection in MRI images using texture features,” in Sensors for Health Monitoring, N. Dey, J. Chaki, and R. Kumar, Eds., Academic Press, 2019, pp. 259–287. doi: https://doi.org/10.1016/B978-0-12-819361-7.00013-0.
  • [47] R. M. Haralick, K. Shanmugam, and I. H. Dinstein, “Textural features for image classification,” IEEE Trans Syst Man Cybern, no. 6, pp. 610–621, 1973.
  • [48] N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” in 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR’05), Ieee, 2005, pp. 886–893.
  • [49] T. Ojala, M. Pietikainen, and D. Harwood, “Performance evaluation of texture measures with classification based on Kullback discrimination of distributions,” in Proceedings of 12th international conference on pattern recognition, IEEE, 1994, pp. 582–585.
  • [50] T. Ojala, M. Pietikäinen, and D. Harwood, “A comparative study of texture measures with classification based on featured distributions,” Pattern Recognit, vol. 29, no. 1, pp. 51–59, 1996.
  • [51] G.-B. Huang, H. Zhou, X. Ding, and R. Zhang, “Extreme learning machine for regression and multiclass classification,” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 42, no. 2, pp. 513–529, 2011.
  • [52] G.-B. Huang, Q.-Y. Zhu, and C.-K. Siew, “Extreme learning machine: theory and applications,” Neurocomputing, vol. 70, no. 1–3, pp. 489–501, 2006.
  • [53] G.-B. Huang, Q.-Y. Zhu, and C.-K. Siew, “Extreme learning machine: a new learning scheme of feedforward neural networks,” in 2004 IEEE international joint conference on neural networks (IEEE Cat. No. 04CH37541), Ieee, 2004, pp. 985–990.
  • [54] V. Vapnik, The nature of statistical learning theory. Springer science & business media, 2013.
  • [55] F. H. Garabaghi, R. Benzer, S. Benzer, and A. Ç. Günal, “Effect of polynomial, radial basis, and Pearson VII function kernels in support vector machine algorithm for classification of crayfish,” Ecol Inform, vol. 72, p. 101911, Dec. 2022, doi: 10.1016/J.ECOINF.2022.101911.
  • [56] S. Arya, D. M. Mount, N. S. Netanyahu, R. Silverman, and A. Y. Wu, “An optimal algorithm for approximate nearest neighbor searching fixed dimensions,” Journal of the ACM (JACM), vol. 45, no. 6, pp. 891–923, 1998.
  • [57] A.-M. Šimundić, “Measures of diagnostic accuracy: basic definitions,” EJIFCC, vol. 19, no. 4, p. 203, 2009.
There are 57 citations in total.

Details

Primary Language English
Subjects Empirical Software Engineering
Journal Section Articles
Authors

Ahmet Saygılı 0000-0001-8625-4842

Project Number NKUBAP.06.GA.21.317
Early Pub Date August 27, 2023
Publication Date August 31, 2023
Submission Date June 5, 2023
Acceptance Date August 10, 2023
Published in Issue Year 2023Volume: 6 Issue: 2

Cite

IEEE A. Saygılı, “Rapid and Precise Identification of COVID-19 through Segmentation and Classification of CT and X-ray Images”, SAUCIS, vol. 6, no. 2, pp. 123–139, 2023, doi: 10.35377/saucis...1309970.
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