Skip to main content
SpringerLink
Log in

An Improved Grey Wolf Optimization–Based Convolutional Neural Network for the Segmentation of COVID-19 Lungs–Infected Parts

  • Published:
Cognitive Computation Aims and scope Submit manuscript

Abstract

The coronavirus outbreak is a recent pandemic that destroyed most of the lives, economy, and livelihoods. The detection of COVID-19 is the main aim to detect and provide better treatment for patients to mitigate its impact. In addition, it is necessary to diagnose the disease swiftly with upgraded technologies. This can be achieved by CT image scanning. This provides the fastest detection of the disease. Moreover, it can also be used to diagnose the percentage of the affected lung areas. To perform this fastly, we propose a novel approach known as Convolutional Neural Network (CNN)–based Improved Grey Wolf Optimization (IGWO) algorithm. The proposed CNN utilizes a SegNet-based approach which can be used to detect the affected area in the lungs by using the encoder and decoder steps. The encoder in this approach uses three types of CNN architecture. First, the decoder is used to reconstruct the images. The overfitting issues during the iterations and complexities are reduced by adopting the IGWO approach. The experimental analysis depicts that the proposed approach effectively segments the CT images and promptly diagnoses the affected lung area.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Availability of Data and Material

Data sharing is not applicable to this article as no new data were created or analyzed in this study.

Code Availability

Not applicable.

References

  1. Perlman S. Another decade, another coronavirus. N Engl J Med. 2020;382(8):760–2.

    Article  Google Scholar 

  2. He F, Deng Y, Li W. Coronavirus disease 2019: what we know? J Med Virol. 2020;92(7):719–25.

    Article  Google Scholar 

  3. World Health Organization. Coronavirus disease 2019 (COVID-19): situation report. 2020;73.

  4. Zu ZY, Jiang MD, Xu PP, Chen W, Ni QQ, Lu GM, Zhang LJ. Coronavirus disease 2019 (COVID-19): a perspective from China. Radiology. 2020;296(2):E15-25.

    Article  Google Scholar 

  5. Allam Z, Dey G, Jones DS. Artificial intelligence (AI) provided early detection of the coronavirus (COVID-19) in China and will influence future urban health policy internationally. Ai. 2020;1(2):156–65.

  6. Pham QV, Nguyen DC, Huynh-The T, Hwang WJ, Pathirana PN. Artificial intelligence (AI) and big data for coronavirus (COVID-19) pandemic: a survey on the state-of-the-arts. IEEE Access. 2020;8:130820.

  7. Gozes O, Frid-Adar M, Sagie N, Zhang H, Ji W, Greenspan H. Coronavirus detection and analysis on chest ct with deep learning. 2020. arXiv preprint: http://arxiv.org/abs/2004.02640.

  8. Ghoshal B, Tucker A. Estimating uncertainty and interpretability in deep learning for coronavirus (COVID-19) detection. 2020. arXiv preprint: http://arxiv.org/abs/2003.10769.

  9. Saood A, Hatem I. COVID-19 lung CT image segmentation using deep learning methods: U-Net versus SegNet. BMC Med Imaging. 2021;21(1):1–10.

    Article  Google Scholar 

  10. Mahdy LN, Ezzat KA, Elmousalami HH, Ella HA, Hassanien AE. Automatic x-ray covid-19 lung image classification system based on multi-level thresholding and support vector machine. 2020. MedRxiv.

  11. Yan Q, Wang B, Gong D, Luo C, Zhao W, Shen J, Shi Q, Jin S, Zhang L, You Z. COVID-19 chest CT image segmentation--a deep convolutional neural network solution. 2020. arXiv preprint: http://arxiv.org/abs/2004.10987.

  12. Ranjbarzadeh R, Jafarzadeh Ghoushchi S, Bendechache M, Amirabadi A, Ab Rahman MN, Baseri Saadi S, Aghamohammadi A, Kooshki Forooshani M. Lung infection segmentation for COVID-19 pneumonia based on a cascade convolutional network from CT images. BioMed Res Int. 2021.

  13. Singh D, Kumar V, Yadav V, Kaur M. Deep neural network-based screening model for COVID-19-infected patients using chest X-ray images. Int J Pattern Recognit Artif Intell. 2021;35(03):2151004.

    Article  Google Scholar 

  14. Castiglione A, Vijayakumar P, Nappi M, Sadiq S, Umer M. Covid-19: Automatic detection of the novel coronavirus disease from ct images using an optimized convolutional neural network. IEEE Trans Industr Inf. 2021;17(9):6480–8.

    Article  Google Scholar 

  15. Castiglione A, Umer M, Sadiq S, Obaidat MS, Vijayakumar P. The role of internet of things to control the outbreak of COVID-19 pandemic. IEEE Internet Things J. 2021;8(21):16072–82.

    Article  Google Scholar 

  16. Vahdat S, Kamal M, Afzali-Kusha A, Pedram M. LATIM: loading-aware offline training method for inverter-based memristive neural networks. IEEE Trans Circuits Syst II Express Briefs. 2021;68(10):3346–50.

    Google Scholar 

  17. Kaiser MS, Mahmud M, Noor MBT, Zenia NZ, Al Mamun S, Mahmud KA, Azad S, Aradhya VM, Stephan P, Stephan T, Kannan R. iWorkSafe: towards healthy workplaces during COVID-19 with an intelligent pHealth App for industrial settings. Ieee Access. 2021;9:13814–28.

    Article  Google Scholar 

  18. Aradhya VM, Mahmud M, Chowdhury M, Guru DS, Kaiser MS, Azad S. Learning through one shot: a phase by phase approach for COVID-19 chest X-ray classification. In 2020 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES) IEEE. 2021;241–44.

  19. Mahmud T, Alam MJ, Chowdhury S, Ali SN, Rahman MM, Fattah SA, Saquib M. CovTANet: a hybrid tri-level attention-based network for lesion segmentation, diagnosis, and severity prediction of COVID-19 chest CT scans. IEEE Trans Industr Inf. 2020;17(9):6489–98.

    Article  Google Scholar 

  20. Mahmud T, Rahman MA, Fattah SA, Kung SY. CovSegNet: a multi encoder–decoder architecture for improved lesion segmentation of COVID-19 chest CT scans. IEEE Trans Artif Intell. 2021;2(3):283–97.

    Article  Google Scholar 

  21. Elharrouss O, Subramanian N, Al-Maadeed S. An encoder–decoder-based method for segmentation of COVID-19 lung infection in CT images. SN Comput Sci. 2022;3(1):1–12.

    Article  Google Scholar 

  22. Fan DP, Zhou T, Ji GP, Zhou Y, Chen G, Fu H, Shen J, Shao L. Inf-net: automatic covid-19 lung infection segmentation from ct images. IEEE Trans Med Imaging. 2020;39(8):2626–37.

    Article  Google Scholar 

  23. Chvetsov AV, Paige SL. The influence of CT image noise on proton range calculation in radiotherapy planning. Phys Med Biol. 2010;55(6):N141.

    Article  Google Scholar 

  24. Patro S, Sahu KK. Normalization: a preprocessing stage. 2015. arXiv preprint:http://arxiv.org/abs/1503.06462.

  25. Gonçalves DN, de Moares Weber VA, Pistori JGB, da Costa Gomes R, de Araujo AV, Pereira MF, Gonçalves WN, Pistori H. Carcass image segmentation using CNN-based methods. Inf Process Agric. 2020.

  26. Tian Z, Shen C, Wang X, Chen H. Boxinst: high-performance instance segmentation with box annotations. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021;5443–52.

  27. Maqsood M, Nazir F, Khan U, Aadil F, Jamal H, Mehmood I, Song OY. Transfer learning assisted classification and detection of Alzheimer’s disease stages using 3D MRI scans. Sensors. 2019;19(11):2645.

    Article  Google Scholar 

  28. Albahli S, Nida N, Irtaza A, Yousaf MH, Mahmood MT. Melanoma lesion detection and segmentation using YOLOv4-DarkNet and active contour. IEEE Access. 2020;8:198403–14.

    Article  Google Scholar 

  29. Qassim H, Verma A, Feinzimer D. Compressed residual-VGG16 CNN model for big data places image recognition. In 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC). IEEE. 2018;169–75.

  30. Carvalho T, De Rezende ER, Alves MT, Balieiro FK, Sovat RB. Exposing computer generated images by eye’s region classification via transfer learning of VGG19 CNN. In 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE. 2017;866–70.

  31. Theckedath D, Sedamkar RR. Detecting affect states using VGG16, ResNet50 and SE-ResNet50 networks. SN Comput Sci. 2020;1(2):1–7.

    Article  Google Scholar 

  32. Ketkar N. Stochastic gradient descent. In Deep Learning with Python. Apress Berkeley CA. 2017;113–32.

  33. Nadimi-Shahraki MH, Taghian S, Mirjalili S. An improved grey wolf optimizer for solving engineering problems. Expert Syst Appl. 2021;166:113917.

  34. Gupta S, Deep K. A novel random walk grey wolf optimizer. Swarm Evol Comput. 2019;44:101–12.

    Article  Google Scholar 

  35. Teixeira LO, Pereira RM, Bertolini D, Oliveira LS, Nanni L, Cavalcanti GD, Costa YM. Impact of lung segmentation on the diagnosis and explanation of COVID-19 in chest X-ray images. Sensors. 2021;21(21):7116.

    Article  Google Scholar 

  36. Chen C, Xiao R, Zhang T, Lu Y, Guo X, Wang J, Chen H, Wang Z. Pathological lung segmentation in chest CT images based on improved random walker. Comput Methods Programs Biomed. 2021;200:105864.

  37. Yao Q, Xiao L, Liu P, Zhou SK. Label-free segmentation of COVID-19 lesions in lung CT. IEEE Trans Med Imaging. 2021;40(10):2808–19.

    Article  Google Scholar 

  38. Saeedizadeh N, Minaee S, Kafieh R, Yazdani S, Sonka M. COVID TV-Unet: segmenting COVID-19 chest CT images using connectivity imposed Unet. Computer Methods and Programs in Biomedicine Update. 2021;1:100007.

  39. Gozes O, Frid-Adar M, Greenspan H, Browning PD, Zhang H, Ji W, Bernheim A, Siegel E. Rapid ai development cycle for the coronavirus (covid-19) pandemic: initial results for automated detection & patient monitoring using deep learning ct image analysis. 2020. arXiv preprint: http://arxiv.org/abs/2003.05037.

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Institute of Aeronautical Engineering, 500043, Hyderabad, Telangana, India

    P. Sridhar

  2. Department of IT, Botho University, Gaborone, Botswana

    Jayaraj Ramasamy

  3. Department of Electronics and Communication Engineering, Jaypee University of Engineering and Technology, Guna, 473226, India

    Ravi Kumar

  4. Department of IT, Vignan’s Foundation for Science, Technology & Research (Deemed to be University), Guntur Dist. AP, 522 213, Vadlamudi, India

    Ramakrishnan Ramanathan

  5. Department of CSE, O P Jindal University, 496109, Punjipathra, Raigarh, Chattisgarh, India

    Rakesh Nayak

  6. Department of Civil Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), 602105, Chennai, Tamil Nadu, India

    M. Tholkapiyan

Authors
  1. P. Sridhar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jayaraj Ramasamy
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ravi Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ramakrishnan Ramanathan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Rakesh Nayak
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. M. Tholkapiyan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to P. Sridhar.

Ethics declarations

Consent to Participate

Informed consent does not apply as this was a retrospective review with no identifying patient information.

Consent for Publication

Not applicable.

Conflict of Interest

The authors declare no competing interests.

Human and Animal Rights

This article does not contain any studies with human or animal subjects performed by any of the authors.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sridhar, P., Ramasamy, J., Kumar, R. et al. An Improved Grey Wolf Optimization–Based Convolutional Neural Network for the Segmentation of COVID-19 Lungs–Infected Parts. Cogn Comput 15, 2175–2188 (2023). https://doi.org/10.1007/s12559-023-10180-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12559-023-10180-1

Keywords

Search

Navigation