Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Arabian Journal for Science and Engineering
Published in: Arabian Journal for Science and Engineering 2/2022

07-10-2021 | Research Article-Computer Engineering and Computer Science

Computer-Aided Detection of COVID-19 from CT Images Based on Gaussian Mixture Model and Kernel Support Vector Machines Classifier

Author: Ahmet Saygılı

Published in: Arabian Journal for Science and Engineering | Issue 2/2022

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

COVID-19 is a virus that has been declared an epidemic by the world health organization and causes more than 2 million deaths in the world. To achieve this, computer-aided automatic diagnosis systems are created on medical images. In this study, an image processing and machine learning-based method is proposed that enables segmenting of CT images taken from COVID-19 patients and automatic detection of the virus through the segmented images. The main purpose of the study is to automatically diagnose the COVID-19 virus. The study consists of three basic steps: preprocessing, segmentation and classification. Image resizing, image sharpening, noise removal, contrast stretching processes are included in the preprocessing phase and segmentation of images with Expectation–Maximization-based Gaussian Mixture Model in the segmentation phase. In the classification stage, COVID-19 is classified as positive and negative by using kNN, decision tree, and two different ensemble methods together with the kernel support vector machines method. In the study, two different CT datasets that are open to the public and a mixed dataset created by combining these datasets were used. The best accuracy values for Dataset-1, Dataset-2 and Mixed Dataset are 98.5%, 86.3%, 94.5%, respectively. The achieved results prove that the proposed approach advances state-of-the-art performance. Within the scope of the study, a GUI that can automatically detect COVID-19 has been created.
previous article To Secure the Communication in Powerful Internet of Things Using Innovative Post-Quantum Cryptographic Method
next article Increasing Participants Using Counting-Based Secret Sharing via Involving Matrices and Practical Steganography

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now
Literature
1.
2.
Fauci, A.S.; Lane, H.C.; Redfield, R.R.: Covid-19—navigating the uncharted. ed: Mass Medical Soc (2020)
3.
Velavan, T.P.; Meyer, C.G.: The COVID-19 epidemic. Trop. Med. Int. Health 25, 278 (2020)CrossRef
4.
Rodriguez-Morales, A.J.; Cardona-Ospina, J.A.; Gutiérrez-Ocampo, E.; Villamizar-Peña, R.; Holguin-Rivera, Y.; Escalera-Antezana, J.P., et al.: Clinical, laboratory and imaging features of COVID-19: a systematic review and meta-analysis. Travel Med. Infect. Dis. 34, 101623 (2020)CrossRef
5.
Borghesi, A.; Maroldi, R.: COVID-19 outbreak in Italy: experimental chest X-ray scoring system for quantifying and monitoring disease progression. Radiol. Med. (Torino) 125, 509–513 (2020)CrossRef
6.
Fang, Y.; Zhang, H.; Xie, J.; Lin, M.; Ying, L.; Pang, P., et al.: Sensitivity of chest CT for COVID-19: comparison to RT-PCR. Radiology 296, E115–E117 (2020)CrossRef
7.
Wong, H.Y.F.; Lam, H.Y.S.; Fong, A.H.-T.; Leung, S.T.; Chin, T.W.-Y.; Lo, C.S.Y., et al.: Frequency and distribution of chest radiographic findings in patients positive for COVID-19. Radiology 296, E72–E78 (2020)CrossRef
8.
Kanne, J.P.; Little, B.P.; Chung, J.H.; Elicker, B.M.; Ketai, L.H.: Essentials for radiologists on COVID-19: an update—radiology scientific expert panel. ed: Radiological Society of North America (2020)
9.
Xie, X.; Zhong, Z.; Zhao, W.; Zheng, C.; Wang, F.; Liu, J.: Chest CT for typical coronavirus disease 2019 (COVID-19) pneumonia: relationship to negative RT-PCR testing. Radiology 296, E41–E45 (2020)CrossRef
10.
Devi, S.S.; Singh, N.H.; Laskar, R.H.: Fuzzy C-means clustering with histogram based cluster selection for skin lesion segmentation using non-dermoscopic images. Int. J. Interact. Multimed. Artif. Intell. 6, 26–31 (2020)
11.
Lee, E.Y.; Ng, M.-Y.; Khong, P.-L.: COVID-19 pneumonia: what has CT taught us? Lancet. Infect. Dis 20, 384–385 (2020)CrossRef
12.
Shi, H.; Han, X.; Jiang, N.; Cao, Y.; Alwalid, O.; Gu, J., et al.: Radiological findings from 81 patients with COVID-19 pneumonia in Wuhan, China: a descriptive study. Lancet. Infect. Dis 20, 425–434 (2020)CrossRef
13.
Borakati, A.; Perera, A.; Johnson, J.; Sood, T.: Diagnostic accuracy of X-ray versus CT in COVID-19: a propensity-matched database study. BMJ Open 10, e042946 (2020)CrossRef
14.
Ng, M.-Y.; Lee, E.Y.; Yang, J.; Yang, F.; Li, X.; Wang, H., et al.: Imaging profile of the COVID-19 infection: radiologic findings and literature review. Radiol. Cardiothoracic Imaging 2, e200034 (2020)CrossRef
15.
Angelov, P.; Soares, E.: Explainable-by-design approach for covid-19 classification via ct-scan. medRxiv (2020)
16.
Hasan, N.; Bao, Y.; Shawon, A.: DenseNet convolutional neural networks application for predicting COVID-19 using CT Image (2020)
17.
Silva, P.; Luz, E.; Silva, G.; Moreira, G.; Silva, R.; Lucio, D., et al.: COVID-19 detection in CT images with deep learning: a voting-based scheme and cross-datasets analysis. Inform. Med. Unlock. 20, 100427 (2020)CrossRef
18.
Soares, E.; Angelov, P.; Biaso, S.; Froes, M.H.; Abe, D.K.: SARS-CoV-2 CT-scan dataset: a large dataset of real patients CT scans for SARS-CoV-2 identification. medRxiv, p. 2020.04.24.20078584 (2020)
19.
Yazdani, S.; Minaee, S.; Kafieh, R.; Saeedizadeh, N.; Sonka, M.: Covid ct-net: predicting covid-19 from chest ct images using attentional convolutional network. arXiv preprintarXiv:​2009.​05096 (2020)
20.
Abbas, A.; Abdelsamea, M M.; Gaber, M.M.: Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network. Appl. Intell., pp. 1–11 (2020)
21.
Joshi, R.C.; Yadav, S.; Pathak, V.K.; Malhotra, H.S.; Khokhar, H.V.S.; Parihar, A., et al.: A deep learning-based COVID-19 automatic diagnostic framework using chest X-ray images. Biocybern. Biomed. Eng. 41, 239–254 (2021)CrossRef
22.
Linda, W.: A tailored deep convolutional neural network design for detection of covid-19 cases from chest radiography images. J. Netw. Comput. Appl. 20, 1–12 (2020)
23.
Ozturk, T.; Talo, M.; Yildirim, E.A.; Baloglu, U.B.; Yildirim, O.; Acharya, U.R.: Automated detection of COVID-19 cases using deep neural networks with X-ray images. Comput. Biol. Med. 121, 103792 (2020)CrossRef
24.
Almalki, Y.E.; Qayyum, A.; Irfan, M.; Haider, N.; Glowacz, A.; Alshehri, F.M., et al.: A novel method for COVID-19 diagnosis using artificial intelligence in chest X-ray images. Healthcare 9, 522 (2021)CrossRef
25.
Irfan, M.; Iftikhar, M.A.; Yasin, S.; Draz, U.; Ali, T.; Hussain, S., et al.: Role of hybrid deep neural networks (HDNNs), computed tomography, and chest X-rays for the detection of COVID-19. Int. J. Environ. Res. Public Health 18, 3056 (2021)CrossRef
26.
Kassani, S.H.; Kassasni, P.H.; Wesolowski, M.J.; Schneider, K.A.; Deters, R.: Automatic detection of coronavirus disease (covid-19) in x-ray and ct images: a machine learning-based approach. arXiv preprintarXiv:​2004.​10641 (2020)
27.
Maghdid, H.S.; Asaad, A.T.; Ghafoor, K.Z.; Sadiq, A.S.; Khan, M.K.: Diagnosing COVID-19 pneumonia from X-ray and CT images using deep learning and transfer learning algorithms. arXiv preprint arXiv:​2004.​00038(2020)
28.
Wang, Z.; Liu, Q.; Dou, Q.: Contrastive cross-site learning with redesigned net for COVID-19 CT classification. IEEE J. Biomed. Health Inform. 24, 2806–2813 (2020)CrossRef
29.
Jaiswal, A.; Gianchandani, N.; Singh, D.; Kumar, V.; Kaur, M.: Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning. J. Biomol. Struct. Dyn., pp. 1–8 (2020)
30.
Hu, S.; Gao, Y.; Niu, Z.; Jiang, Y.; Li, L.; Xiao, X., et al.: Weakly supervised deep learning for covid-19 infection detection and classification from ct images. IEEE Access 8, 118869–118883 (2020)CrossRef
31.
Wu, Y.-H.; Gao, S.-H.; Mei, J.; Xu, J.; Fan, D.-P.; Zhao, C.-W., et al.: JCS: an explainable COVID-19 diagnosis system by joint classification and segmentation. arXiv preprint arXiv:​2004.​07054(2020)
32.
Sun, L.; Mo, Z.; Yan, F.; Xia, L.; Shan, F.; Ding, Z., et al.: Adaptive feature selection guided deep forest for covid-19 classification with chest ct. IEEE J. Biomed. Health Inform. 24, 2798–2805 (2020)CrossRef
33.
Mishra, N.K.; Singh, P.; Joshi, S.D.: Automated detection of COVID-19 from CT scan using convolutional neural network. Biocybern. Biomed. Eng. (2021)
34.
Brunese, L.; Martinelli, F.; Mercaldo, F.; Santone, A.: Machine learning for coronavirus COVID-19 detection from chest x-rays. Procedia Comput. Sci. 176, 2212–2221 (2020)CrossRef
35.
Barstugan, M.; Ozkaya, U.; Ozturk, S.: Coronavirus (covid-19) classification using ct images by machine learning methods. arXiv preprint arXiv:​2003.​09424(2020)
36.
Mohammed, M.A.; Abdulkareem, K.H.; Garcia-Zapirain, B.; Mostafa, S.A.; Maashi, M.S.; Al-Waisy, A.S., et al.: A comprehensive investigation of machine learning feature extraction and classification methods for automated diagnosis of covid-19 based on x-ray images. Comput. Mater. Contin. 66 (2020)
37.
Khan, N.; Ullah, F.; Hassan, M.A.; Hussain, A.: COVID-19 classification based on Chest X-Ray images using machine learning techniques. J. Comput. Sci. Technol. Stud. 2, 01–11 (2020)
38.
Yang, X.; He, X.; Zhao, J.; Zhang, Y.; Zhang, S.; Xie, P.: COVID-CT-dataset: a CT scan dataset about COVID-19. arXiv preprint arXiv:​2003.​13865(2020)
39.
Lim, J.S.; Oppenheim, A.V.: Enhancement and bandwidth compression of noisy speech. Proc. IEEE 67, 1586–1604 (1979)CrossRef
40.
Dalal, N.; Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), pp. 886–893 (2005)
41.
Arya, S.; Mount, D.M.; Netanyahu, N.S.; Silverman, R.; Wu, A.Y.: An optimal algorithm for approximate nearest neighbor searching fixed dimensions. JACM 45, 891–923 (1998)MathSciNetCrossRef
42.
Glowacz, A.: Ventilation diagnosis of angle grinder using thermal imaging. Sensors 21, 2853 (2021)CrossRef
43.
Barandela, R.; Valdovinos, R.M.; Sánchez, J.S.: New applications of ensembles of classifiers. Pattern Anal. Appl. 6, 245–256 (2003)MathSciNetCrossRef
44.
Opitz, D.; Maclin, R.: Popular ensemble methods: An empirical study. J. Artif. Intell. Res. 11, 169–198 (1999)CrossRef
45.
Bay, S.D.: Combining nearest neighbor classifiers through multiple feature subsets. In: ICML, pp. 37–45 (1998)
46.
Ashour, A.S.; Guo, Y.; Hawas, A.R.; Xu, G.: Ensemble of subspace discriminant classifiers for schistosomal liver fibrosis staging in mice microscopic images. Health Inf. Sci. Syst. 6, 1–10 (2018)CrossRef
47.
Kuncheva, L.I.; Rodríguez, J.J.; Plumpton, C.O.; Linden, D.E.; Johnston, S.J.: Random subspace ensembles for fMRI classification. IEEE Trans. Med. Imaging 29, 531–542 (2010)CrossRef
48.
Rish, I.: An empirical study of the naive Bayes classifier. In: IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence, pp. 41–46 (2001)
49.
Noble, W.S.: What is a support vector machine? Nat. Biotechnol. 24, 1565–1567 (2006)CrossRef
50.
Hofmann, M.: Support vector machines-kernels and the kernel trick. Notes 26, 1–16 (2006)
51.
Schölkopf, B.; Smola, A.J.; Bach, F.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge (2002)
52.
Hussain, M.; Wajid, S.K.; Elzaart, A.; Berbar, M.: A comparison of SVM kernel functions for breast cancer detection. In: 2011 Eighth International Conference Computer Graphics, Imaging and Visualization, pp. 145–150 (2011)
53.
Šimundić, A.-M.: Measures of diagnostic accuracy: basic definitions. EJIFCC 19, 203–211 (2009)
54.
van Stralen, K.J.; Stel, V.S.; Reitsma, J.B.; Dekker, F.W.; Zoccali, C.; Jager, K.J.: Diagnostic methods I: sensitivity, specificity, and other measures of accuracy. Kidney Int. 75, 1257–1263 (2009)CrossRef
55.
Coulthard, M.G.: Quantifying how tests reduce diagnostic uncertainty. Arch. Dis. Child. 92, 404–408 (2007)CrossRef
56.
Boughorbel, S.; Jarray, F.; El-Anbari, M.: Optimal classifier for imbalanced data using Matthews Correlation Coefficient metric. PLoS ONE 12, e0177678 (2017)CrossRef
57.
Khan, K.B.; Siddique, M.S.; Ahmad, M.; Mazzara, M.: A hybrid unsupervised approach for retinal vessel segmentation. BioMed Res. Int. 2020 (2020)
58.
Fan, J.; Upadhye, S.; Worster, A.: Understanding receiver operating characteristic (ROC) curves. Can. J. Emerg. Med. 8, 19–20 (2006)CrossRef
Metadata
Title
Computer-Aided Detection of COVID-19 from CT Images Based on Gaussian Mixture Model and Kernel Support Vector Machines Classifier
Author
Ahmet Saygılı
Publication date
07-10-2021
Publisher
Springer Berlin Heidelberg
Published in
Arabian Journal for Science and Engineering / Issue 2/2022
Print ISSN: 2193-567X
Electronic ISSN: 2191-4281
DOI
https://doi.org/10.1007/s13369-021-06240-z

Other articles of this Issue 2/2022

Arabian Journal for Science and Engineering 2/2022 Go to the issue

Research Article-Computer Engineering and Computer Science

A Two-stage Method of Synchronization Prediction Framework in TDD

Research Article-Computer Engineering and Computer Science

Deep Transfer Learning Based Unified Framework for COVID19 Classification and Infection Detection from Chest X-Ray Images

Research Article-Computer Engineering and Computer Science

Increasing Participants Using Counting-Based Secret Sharing via Involving Matrices and Practical Steganography

Research Article-Computer Engineering and Computer Science

Design and Implementation of a Prediction Approach Using Big Data and Deep Learning Techniques for Parking Occupancy

Research Article-Computer Engineering and Computer Science

Bio-Inspired Modular Relative Jacobian for Holistically Controlled Four-Arm Manipulators Using Opposite and Adjacent Dual-Arm Pairs

Research Article-Computer Engineering and Computer Science

A New Ensemble-Based Intrusion Detection System for Internet of Things

Premium Partners

    Image Credits