Skip to main content

Advertisement

Springer Nature Link
Log in

Chaotic Satin Bowerbird Optimizer Based Advanced AI Techniques for Detection of COVID-19 Diseases from CT Scans Images

  • Research Paper
  • Published:
New Generation Computing Aims and scope Submit manuscript

A Correction to this article was published on 16 October 2024

This article has been updated

Abstract

Background

The SARS-CoV-2 virus, which caused the COVID-19 pandemic, emerged in late 2019, leading to significant global health challenges due to the lack of targeted treatments and the need for rapid diagnosis.

Aim/objective

This study aims to develop an AI-based system to accurately detect COVID-19 from CT scans, enhancing the diagnostic process.

Methodology

We employ a faster region-based convolutional neural network (faster R-CNN) for extracting features from pre-processed CT images and use the chaotic satin bowerbird optimization algorithm (CSBOA) for fine-tuning the model parameters.

Results

Our experimental results show high performance in terms of precision, recall, accuracy, and f-measure, effectively identifying COVID-19 affected areas in CT images. The suggested model attained 91.78% F1-score, 91.37% accuracy, 91.87% precision, and 90.3% recall with a learning rate of 0.0001.

Conclusion

This method contributes to the advancement of AI-driven diagnostic tools, providing a pathway for improved early detection and treatment strategies for COVID-19, thus aiding in better clinical management.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Algorithm 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Explore related subjects

Discover the latest articles and news from researchers in related subjects, suggested using machine learning.

Change history

References

  1. Shahin, O.R., Alshammari, H.H., Taloba, A.I., Abd El-Aziz, R.M.: Machine learning approach for autonomous detection and classification of COVID-19 virus. Comput. Electr. Eng.. Electr. Eng. 101, 108055 (2022)

    Article  Google Scholar 

  2. Iwendi, C., Mahboob, K., Khalid, Z., Javed, A.R., Rizwan, M., Ghosh, U.: Classification of COVID-19 individuals using adaptive neuro-fuzzy inference system. Multimed. Syst. (2022). https://doi.org/10.1007/s00530-021-00774-w

    Article  Google Scholar 

  3. Lu, S., Zhu, Z., Gorriz, J.M., Wang, S.H., Zhang, Y.D.: NAGNN: classification of COVID-19 based on neighboring aware representation from deep graph neural network. Int. J. Intell. Syst.Intell. Syst. 37(2), 1572–1598 (2022)

    Article  Google Scholar 

  4. Althenayan, A.S., AlSalamah, S.A., Aly, S., Nouh, T., Mirza, A.A.: Detection and classification of COVID-19 by radiological imaging modalities using deep learning techniques: a literature review. Appl. Sci. 12(20), 10535 (2022)

    Article  Google Scholar 

  5. Bhandari, M., Shahi, T.B., Siku, B., Neupane, A.: Explanatory classification of CXR images into COVID-19, Pneumonia and Tuberculosis using deep learning and XAI. Comput. Biol. Med.. Biol. Med. 1(150), 106156 (2022)

    Article  Google Scholar 

  6. Barshooi, A.H., Amirkhani, A.: A novel data augmentation based on Gabor filter and convolutional deep learning for improving the classification of COVID-19 chest X-Ray images. Biomed. Signal Process. Control 72, 103326 (2022)

    Article  Google Scholar 

  7. Malik, H., Anees, T.: BDCNet: Multi-classification convolutional neural network model for classification of COVID-19, pneumonia, and lung cancer from chest radiographs. Multimed. Syst. 28(3), 815–829 (2022)

    Article  Google Scholar 

  8. Kumar, R., Arora, R., Bansal, V., Sahayasheela, V.J., Buckchash, H., Imran, J., et al.: Classification of COVID-19 from chest x-ray images using deep features and correlation coefficient. Multimed. Tools Appl. 81(19), 27631–27655 (2022)

    Article  Google Scholar 

  9. Amin, J., Anjum, M.A., Sharif, M., Rehman, A., Saba, T., Zahra, R.: Microscopic segmentation and classification of COVID-19 infection with ensemble convolutional neural network. Microsc. Res. Tech.. Res. Tech. 85(1), 385–397 (2022)

    Article  Google Scholar 

  10. Baghdadi, N.A., Malki, A., Abdelaliem, S.F., Balaha, H.M., Badawy, M., Elhosseini, M.: An automated diagnosis and classification of COVID-19 from chest CT images using a transfer learning-based convolutional neural network. Comput. Biol. Med.. Biol. Med. 144, 105383 (2022)

    Article  Google Scholar 

  11. Ayalew, A.M., Salau, A.O., Abeje, B.T., Enyew, B.: Detection and classification of COVID-19 disease from X-ray images using convolutional neural networks and histogram of oriented gradients. Biomed. Signal Process. Control Signal Process. Control 74, 103530 (2022)

    Article  Google Scholar 

  12. Pustokhin, D.A., Pustokhina, I.V., Dinh, P.N., Phan, S.V., Nguyen, G.N., Joshi, G.P.K.S.: An effective deep residual network based class attention layer with bidirectional LSTM for diagnosis and classification of COVID-19. J. Appl. Stat. 50(3), 477–494 (2023)

    Article  MathSciNet  Google Scholar 

  13. Kumar, S., Gupta, S.K., Kumar, V., Kumar, M., Chaube, M.K., Naik, N.S.: Ensemble multimodal deep learning for early diagnosis and accurate classification of COVID-19. Comput. Electr. Eng.. Electr. Eng. 103, 108396 (2022)

    Article  Google Scholar 

  14. Aggarwal, P., Mishra, N.K., Fatimah, B., Singh, P., Gupta, A., Joshi, S.D.: COVID-19 image classification using deep learning: advances, challenges and opportunities. Comput. Biol. Med.. Biol. Med. 144, 105350 (2022)

    Article  Google Scholar 

  15. Kumar, S., Chaube, M.K., Alsamhi, S.H., Gupta, S.K., Guizani, M., Gravina, R., Fortino, G.: A novel multimodal fusion framework for early diagnosis and accurate classification of COVID-19 patients using X-ray images and speech signal processing techniques. Comput. Methods Programs Biomed.. Methods Programs Biomed. 226, 107109 (2022)

    Article  Google Scholar 

  16. Samee, N.A., El-Kenawy, E.S.M., Atteia, G., Jamjoom, M.M., Ibrahim, A., Abdelhamid, A.A., et al.: Metaheuristic optimization through deep learning classification of COVID-19 in chest X-ray images. Comput. Mater. Contin. 73(2), 4193–4210 (2022)

    Google Scholar 

  17. Zhou, W., Wang, J., Wang, Y., Liu, Z., Yang, C.: CGS-Net: A classification-guided framework for automated infection segmentation of COVID-19 from CT images. Int. J. Imaging Syst. Technol. 34(1), e23021 (2024)

    Article  Google Scholar 

  18. Sahoo, P., Saha, S., Sharma, S.K., Mondal, S., Gowda, S.: A Multi-stage framework for COVID-19 detection and severity assessment from chest radiography images using advanced fuzzy ensemble technique. Expert Syst. Appl. 238, 121724 (2024)

    Article  Google Scholar 

  19. Salazar-Urbina, A., Ventura-Molina, E.J., Yáñez-Márquez, C., Aldape-Pérez, M., López-Yáñez, I.: MiniCovid-Unet: CT-scan lung images segmentation for COVID-19 identification. Comput Sist (2024). https://doi.org/10.13053/cys-28-1-4697

    Article  Google Scholar 

  20. Shankar, K., Mohanty, S.N., Yadav, K., Gopalakrishnan, T.: Automated COVID-19 diagnosis and classification using convolutional neural network with fusion-based feature extraction model. Cogn. Neurodyn. 16(1) (2021). https://doi.org/10.1007/s11571-021-09712-y. ISSN: 1871-4099

  21. Amin, J., Anjum, M.A., Gul, N., Sharif, M.I., Sharif, M.I., Kadry, S.: Localization model and rank-based features selection approach for the classification of GGO and consolidation stages of COVID-19. Expert Syst. Appl. 239, 122317 (2024)

    Article  Google Scholar 

  22. Divya, D., Thilagu, M.: Region growing based K-means clustering and optimal weight prior-attention residual learning for segmentation and classification of COVID-19 CT images. ECTI Trans Comput Inf Technol 18(1), 76–88 (2024)

    Google Scholar 

  23. Ağralı, M., Kılıç, V.: U-TranSvision: transformer-based deep supervision approach for COVID-19 lesion segmentation on computed tomography images. Biomed. Signal Process. Control 93, 106167 (2024)

    Article  Google Scholar 

  24. Alhassan, A.M.: Thresholding chaotic butterfly optimization algorithm with gaussian kernel (TCBOGK) based segmentation and DeTrac deep convolutional neural network for COVID-19 X-ray images. Multimed. Tools Appl. 83, 1–24 (2024)

    Article  Google Scholar 

  25. O. Paiva, Helping radiologists to help people in more than 100 countries! coronavirus cases, CORONACASES.ORG, 2020

  26. Y. Glick, Viewing playlist: COVID-19 Pneumonia, Radiopaedia.Org. 2020

  27. M. Jun, G. Cheng, W. Yixin, A. Xingle, G. Jiantao, Y. Ziqi, Z. Minqing, L. Xin, D. Xueyuan, C. Shucheng, W. Hao, M. Sen, Y. Xiaoyu, N. Ziwei, L. Chen, T. Lu, Z. Yuntao, Z. Qiongjie, D. Guoqiang, H. Jian, COVID-19 CT lung and infection segmentation dataset, Zenodo, Ed., Verson 1.0 ed, 2020

  28. Baswaraju, S., Maheswari, V.U., Chennam, K.K., Thirumalraj, A., Kantipudi, M.P., Aluvalu, R.: Future food production prediction using AROA based hybrid deep learning model in agri-sector. Human-Centric Intell. Syst. 3(4), 521–536 (2023)

    Article  Google Scholar 

  29. Ara, S., Das, A., Dey, A.: Malignant and benign breast cancer classification using machine learning algorithms. In: Proceedings of the 2021 international conference on artificial intelligence (ICAI), Islamabad, Pakistan, pp. 97–101. (2021)

    Google Scholar 

  30. Moosavi, S.H.S., Bardsiri, V.K.: Satin bowerbird optimizer: a new optimization algorithm to optimize ANFIS for software development effort estimation. Eng. Appl. Artif. Intell.Artif. Intell. 60, 1–15 (2017)

    Article  Google Scholar 

  31. Agarwal, N., Mohanty, S.N., Sankhwar, S., et al.: A novel model to predict the effects of enhanced students’ computer interaction on their health in COVID-19 pandemics. New Gen. Comput. 41, 635–668 (2023). https://doi.org/10.1007/s00354-023-00224-3

    Article  Google Scholar 

  32. Satapathy, S.K., Saravanan, S., Mishra, S., Mohanty, S.N.: A comparative analysis of multidiemnsional COVID-19 poverty determinants: an observational machine learning approach. New Gen Comput (2023). https://doi.org/10.1007/s00354-023-00203-8

    Article  Google Scholar 

  33. Sah, S., Surendiran, Dhanalakshmi, R., Mohanty, S.N., Alenezi, F., Polat, K.: Forecasting COVID-19 pandemic using prophet, ARIMA, and hybrid stacked LSTM-GRU models in India. Comput. Math. Methods Med.. Math Methods Med. 2022, 1556025 (2022). https://doi.org/10.1155/2022/1556025

    Article  Google Scholar 

  34. Shome, D., Kar, T., Mohanty, S.N., Tiwari, P., Muhammad, K., AlTameem, A., Zhang, Y., Jilani Saudagar, A.K.: COVID-Transformer: interpretable COVID-19 detection using vision transformer for healthcare. Int. J. Environ. Res. Public Health 18(21), 1–14 (2021). https://doi.org/10.3390/ijerph182111086

    Article  Google Scholar 

  35. Mishra, S., Satapathy, S.K., Cho, S.B., Mohanty, S.N., et al.: Advancing COVID-19 poverty estimation with satellite imagery-based deep learning techniques: a systematic review. Spat. Inf. Res. (2024). https://doi.org/10.1007/s41324-024-00584-y

    Article  Google Scholar 

  36. Palaniappana, R.: Post-covid trends in manufacturing sector and its implications for businesses and policymakers. J. Eng. Manag. Inf. Technol. 2(1), 9–15 (2024). https://doi.org/10.61552/JEMIT.2024.01.002

    Article  Google Scholar 

  37. Shankar, K., Mohanty, S.N., Yadav, K., Gopalakrishnan, T.: Automated COVID-19 diagnosis and classification using convolutional neural network with fusion based feature extraction model. Cogn. Neurodyn. Neurodyn (2021). https://doi.org/10.1007/s11571-021-09712-y

    Article  Google Scholar 

Download references

Funding

No funding associate with this research work.

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Chaitanya Bharathi Institute of Technology, Hyderabad, India

    V. Uma Maheswari

  2. Department of Electronics and Communication Engineering, K. Ramakrishnan College of Engineering, Trichy, India

    S. Stephe

  3. Department of Computer Science Engineering, K. Ramakrishnan College of Technology, Trichy, India

    Arunadevi Thirumalraj

  4. School of Computer Science and Engineering (SCOPE), VIT-AP University, Andhra Pradesh, India

    Sachi Nandan Mohanty

  5. Symbiosis Institute of Technology, Hyderabad Campus, Hyderabad, Symbiosis International University, Pune, India

    Rajanikanth Aluvalu

Authors
  1. V. Uma Maheswari
    View author publications

    Search author on:PubMed Google Scholar

  2. S. Stephe
    View author publications

    Search author on:PubMed Google Scholar

  3. Rajanikanth Aluvalu
    View author publications

    Search author on:PubMed Google Scholar

  4. Arunadevi Thirumalraj
    View author publications

    Search author on:PubMed Google Scholar

  5. Sachi Nandan Mohanty
    View author publications

    Search author on:PubMed Google Scholar

Corresponding author

Correspondence to Sachi Nandan Mohanty.

Ethics declarations

Conflict of Interests

There is no conflict of interest among the authors.

Ethical approval

No ethical approval needs for this study.

Additional information

Publisher's Note

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

The original online version of this article was revised to correct the third author affiliation.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Uma Maheswari, V., Stephe, S., Aluvalu, R. et al. Chaotic Satin Bowerbird Optimizer Based Advanced AI Techniques for Detection of COVID-19 Diseases from CT Scans Images. New Gener. Comput. 42, 1065–1087 (2024). https://doi.org/10.1007/s00354-024-00279-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00354-024-00279-w

Keywords