SpringerLink
Log in

Novel COVID-19 Recognition Framework Based on Conic Functions Classifier

  • Chapter
  • First Online:
Healthcare Informatics for Fighting COVID-19 and Future Epidemics

Part of the book series: EAI/Springer Innovations in Communication and Computing ((EAISICC))

Abstract

The new coronavirus has been declared as a global emergency. The first case was officially declared in Wuhan, China, during the end of 2019. Since then, the virus has spread to nearly every continent, and case numbers continue to rise. The scientists and engineers immediately responded to the virus and presented techniques, devices and treatment approaches to fight back and eliminate the virus. Machine learning is a popular scientific tool and is applied to several medical image recognition problems, involving tumour recognition, cancer detection, organ transplantation and COVID-19 diagnosis. It is proved that machine learning presents robust, fast and accurate results in various medical image recognition problems. Generally, machine learning-based frameworks consist of two stages: feature extraction and classification. In the feature extraction, overwhelmingly unsupervised learning techniques are applied to reduce the input data’s size. This step extracts appropriate features by reducing the computational time and increasing the performance of the classifiers. A classifier is the second step that aims to categorise the input. Within the proposed step, the unsupervised part relies on the feature extraction by using local binary patterns (LBP), followed by feature selection relying on factor analysis technique. The LBP is a kind of visual descriptor, mainly applied for image recognition problem. The aim of using LBP is to analyse the input COVID-19 image and extract salient features. Furthermore, factor analysis is a statistical technique applied to define variability among observed variables in less unnoticed variables named factors. The factor analysis applied to the LBP wavelet aims to select sensitive features from input data (LBP output) and reduce the size input. In the last stage, conic functions classifier is applied to classify two sets of data, categorising the extracted features by using LBP and factor analysis as positive or negative COVID-19 cases.

The proposed solution aims to diagnose COVID-19 by using LBP and factor analysis, based on conic functions classifier. The conic functions classifier presents remarkable results compared with these popular classifiers and state-of-the-art studies presented in the literature.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 119.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 159.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info
Hardcover Book
USD 159.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. E.A. Severo, J.C.F. De Guimarães, M.L. Dellarmelin, Impact of the COVID-19 pandemic on environmental awareness, sustainable consumption and social responsibility: Evidence from generations in Brazil and Portugal. J. Clean. Prod. 286, 124947 (2020). https://doi.org/10.1016/j.jclepro.2020.124947. ISSN 0959-6526

    Article  Google Scholar 

  2. W. Heo, A. Rabbani, J.E. Grable, An evaluation of the effect of the COVID-19 pandemic on the risk tolerance of financial decision makers. Financ. Res. Lett. 2020, 101842 (2020). https://doi.org/10.1016/j.frl.2020.101842. ISSN 1544-6123

    Article  Google Scholar 

  3. F. Altuntas, M.S. Gok, The effect of COVID-19 pandemic on domestic tourism: A DEMATEL method analysis on quarantine decisions. Int. J. Hosp. Manage. 92, 102719 (2022). https://doi.org/10.1016/j.ijhm.2020.102719. ISSN 0278-4319

    Article  Google Scholar 

  4. General Office of National Health Committee, Of a Program for the Diagnosis and Treatment Notice on the Issuance of Novel Coronavirus (2019-nCoV) Infected Pneumonia (trial sixth edition) (2020-02-18). http://www.nhc.gov.cn/yzygj/s7653p/202002/8334a8326dd94d329df351d7da8aefc2.shtml?from=timeline. Accessed 24 Feb 2020

  5. M. Chung, A. Bernheim, X. Mei, et al., CT imaging features of 2019 novel coronavirus (2019-nCoV). Radiology (2020). https://doi.org/10.1148/radiol.2020200230

  6. P. Huang, T. Liu, L. Huang, et al., Use of chest CT in combination with negative RT-PCR assay for the 2019 novel coronavirus but high clinical suspicion. Radiology (2020). https://doi.org/10.1148/radiol.2020200330

  7. P. Puri, N. Comfere, L.A. Drage, H. Shamim, S.A. Bezalel, M.R. Pittelkow, M.D.P. Davis, M. Wang, A.R. Mangold, M.M. Tollefson, J.S. Lehman, A. Meves, J.A. Yiannias, C.C. Otley, R.E. Carter, O. Sokumbi, M.R. Hall, A.G. Bridges, D.H. Murphree, Deep learning for dermatologists: Part II. Current applications. J. Am. Acad. Dermatol. (2020). https://doi.org/10.1016/j.jaad.2020.05.053. ISSN 0190-9622

  8. Y. Tian, F. Saiji, A descriptive framework for the field of deep learning applications in medical images. Knowl. Based Syst. 210, 106445 (2020). https://doi.org/10.1016/j.knosys.2020.106445. ISSN 0950-7051

    Article  Google Scholar 

  9. C. Stefano, S. Shanmukh, C. Federico, Applications of deep learning in dentistry. Oral Surg. Oral Med. Oral Pathol. Oral Radiol. (2020). https://doi.org/10.1016/j.oooo.2020.11.003. ISSN 2212-4403

  10. Z. Tong, J. Gao, D. Yuan, Advances of deep learning applications in ground-penetrating radar: A survey. Construct. Build. Mater. 258, 120371 (2020). https://doi.org/10.1016/j.conbuildmat.2020.120371. ISSN 0950-0618

    Article  Google Scholar 

  11. R. Lu, X. Zhao, J. Li, P. Niu, B. Yang, H. Wu, et al., Genomic characterisation and epidemiology of 2019 novel coronavirus: Implications for virus origins and receptor binding. Lancet 395(10224), 565–574 (2020)

    Article  Google Scholar 

  12. H. Panwar, P.K. Gupta, M.K. Siddiqui, R. Morales-Menendez, V. Singh, Application of deep learning for fast detection of COVID-19 in X-rays using nCOVnet. Chaos Solitons Fractals 138, 109944 (2020). https://doi.org/10.1016/j.chaos.2020.109944

    Article  MathSciNet  Google Scholar 

  13. T. Zebin, S. Rezvy, COVID-19 detection and disease progression visualization: Deep learning on chest X-rays for classification and coarse localization. Appl. Intell. (2020). https://doi.org/10.1007/s10489-020-01867-1

  14. M.A. Elaziz, K.M. Hosny, A. Salah, M.M. Darwish, S. Lu, et al., New machine learning method for image-based diagnosis of COVID-19. PLoS One 15(6), e0235187 (2020). https://doi.org/10.1371/journal.pone.0235187

    Article  Google Scholar 

  15. M.Z.C. Azemin, R. Hassan, M.I.M. Tamrin, M.A.M. Ali, COVID-19 deep learning prediction model using publicly available radiologist-adjudicated chest X-ray images as training data: Preliminary findings. Int. J. Biomed. Imag. 2020, 8828855 (2020). https://doi.org/10.1155/2020/8828855

    Article  Google Scholar 

  16. K. Kaplan, Y. Kaya, M. Kuncan, H.M. Ertunç, Brain tumor classification using modified local binary patterns (LBP) feature extraction methods. Med. Hypotheses 139, 109696 (2020). https://doi.org/10.1016/j.mehy.2020.109696. ISSN 0306-9877

    Article  Google Scholar 

  17. K. Kaplan, Y. Kaya, M. Kuncan, M.R. Minaz, H.M. Ertunç, An improved feature extraction method using texture analysis with LBP for bearing fault diagnosis. Appl. Soft Comput. 87, 106019 (2020). https://doi.org/10.1016/j.asoc.2019.106019. ISSN 1568-4946

    Article  Google Scholar 

  18. J. Tang, Q. Su, B. Su, S. Fong, W. Cao, X. Gong, Parallel ensemble learning of convolutional neural networks and local binary patterns for face recognition. Comput. Methods Progr. Biomed. 197, 105622 (2020). https://doi.org/10.1016/j.cmpb.2020.105622. ISSN 0169-2607

    Article  Google Scholar 

  19. A. Güner, Ö.F. Alçin, A. Şengür, Automatic digital modulation classification using extreme learning machine with local binary pattern histogram features. Measurement 145, 214–225 (2019). https://doi.org/10.1016/j.measurement.2019.05.061. ISSN 0263-2241

    Article  Google Scholar 

  20. D. Salas-Gonzalez, J. Górriz, J. Ramírez, I. Illán, M. López, F. Segovia, et al., Feature selection using factor analysis for Alzheimer’s diagnosis using F18-FDG PET images. Med. Phys. 37(11), 6084–6095 (2010). https://doi.org/10.1118/1.3488894

    Article  Google Scholar 

  21. M. Usman, S. Ahmed, J. Ferzund, A. Mehmood, A. Rehman, Using PCA and factor analysis for dimensionality reduction of bio-informatics data. Int. J. Adv. Comput. Sci. Appl. 8(5) (2017). https://doi.org/10.14569/ijacsa.2017.080551

  22. R.N. Gasimov, G. Ozturk, Separation via polyhedral conic functions. Optim. Methods Softw. 21(4), 527–540 (2006)

    Article  MathSciNet  Google Scholar 

  23. E. Çimen, A random subspace based conic functions ensemble classifier. Turk. J. Electr. Eng. Comput. Sci. 28(4), 2165–2182 (2020). https://doi.org/10.3906/elk-1911-89

    Article  Google Scholar 

  24. J.P. Cohen, P. Morrison, L. Dao, COVID-19 image data collection. ar**v.2020, https://github.com/ieee8023/covid-chestxray-dataset

  25. L. Wang, A. Wong, COVID-Net: A tailored deep convolutional neural network design for detection of COVID-19 cases from chest radiography images. Ar**v 2200309871 (2020)

    Google Scholar 

  26. X. Li, D. Zhu, COVID-Xpert: An AI powered population screening of COVID-19 cases using chest radiography images. Ar**v:200403042 1–6 (2020)

    Google Scholar 

  27. P. Afshar, S. Heidarian, F. Naderkhani, A. Oikonomou, K.N. Plataniotis, A. Mohammadi et al., -Caps: A capsule network-based framework for identification of Covid-19 cases from X-ray images. Ar**v Prepr Ar**v200402696 1–4 (2020)

    Google Scholar 

  28. M. Farooq, A. Hafeez, “COVID-ResNet: A Deep Learning Framework for Screening of COVID19 from Radiographs”, Ar**v:2003.14395, 2020

    Google Scholar 

  29. T. Ozturk, M. Talo, E.A. Yildirim, U.B. Baloglu, O. Yildirim, U. Rajendra Acharya, Automated detection of COVID-19 cases using deep neural networks with X-ray images. Comput. Biol. Med. 121, 103792 (2020)

    Article  Google Scholar 

  30. A.I. Khan, J.L. Shah, M.M. Bhat, Coronet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images. Comput. Methods Prog. Biomed. 196, 105581 (2020)

    Article  Google Scholar 

  31. L. Wang, A. Wong, COVID-Net: A tailored deep convolutional neural network design for detection of COVID-19 cases from chest radiography images. ar**v preprint ar**v:2003.09871 (2020)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Engineering Department, AYBU, Ankara, Turkey

    Ahmad M. Karim

  2. Molde University College-Specialized University in Logistics, Molde, Norway

    Alok Mishra

  3. Software Engineering Department, Atilim University, Ankara, Turkey

    Alok Mishra

Authors
  1. Ahmad M. Karim
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alok Mishra
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alok Mishra .

Editor information

Editors and Affiliations

  1. Information and Communication Technology, University of Malta, Msida, Malta

    Lalit Garg

  2. Electronics & Communication Engineering, Birla Institute of Technology, Jharkhand, India

    Chinmay Chakraborty

  3. University of Mons, Mons, Belgium

    Saïd Mahmoudi

  4. Harrisburg University of Science and Tec, Harrisburg, PA, USA

    Victor S. Sohmen

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Karim, A.M., Mishra, A. (2022). Novel COVID-19 Recognition Framework Based on Conic Functions Classifier. In: Garg, L., Chakraborty, C., Mahmoudi, S., Sohmen, V.S. (eds) Healthcare Informatics for Fighting COVID-19 and Future Epidemics. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-72752-9_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-72752-9_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-72751-2

  • Online ISBN: 978-3-030-72752-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation