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COV-XDCNN: Deep Learning Model with External Filter for Detecting COVID-19 on Chest X-Rays

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Computer, Communication, and Signal Processing (ICCCSP 2022)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 651))

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Abstract

COVID-19 is a highly infective viral disease and it is observed that the newest strains of the SARS-CoV-2 virus has greater infectivity rate. Due to the present pandemic, the economy of the country, the mental and physical state of the people and their regular lives are being affected. Medical studies have shown that the lungs of the patients who are infected by the corona virus are mostly being affected. Chest x-ray or radiography is observed to be one of the most effective imaging techniques for diagnosing problems which are related to the lungs. The study proposes a novel COV-XDCNN model with external filter for diagnosing diseases such as COVID-19, Viral Pneumonia, automatically which can assist the healthcare workers, mainly during the time of outbreak. The motivation of this research lies in designing an automated system which can aid the healthcare workers. The proposed model with external filter gives 97.86% test accuracy in classifying the chest radiography images. The model performance is examined with various other models such as NASNetMobile, ResNet50, MobileNet, VGG-16 etc. and analyzed. The model proposed in this study shows better performance than most of the existing traditional methods.

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References

  1. Farncombe, T., Iniewski, K. (eds.): Medical imaging: technology and applications. CRC Press, Boca Raton (2017)

    Google Scholar 

  2. Chandra, T.B., Verma, K., Singh, B.K., Jain, D., Netam, S.S.: Coronavirus disease (COVID-19) detection in chest X-ray images using majority voting based classifier ensemble. Expert Syst. Appl. 165, 113909 (2021)

    Article  Google Scholar 

  3. Maghdid, H.S., Asaad, A.T., Ghafoor, K.Z., Sadiq, A.S., Mirjalili, S., Khan, M.K.: Diagnosing COVID-19 pneumonia from X-ray and CT images using deep learning and transfer learning algorithms. In: Multimodal Image Exploitation and Learning 2021, vol. 11734, p. 117340E. International Society for Optics and Photonics (2021)

    Google Scholar 

  4. 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(2), 01–11 (2020)

    Google Scholar 

  5. Huang, C., et al.: Clinical features of patients infected with 2019 novel coronavirus in Wuhan China. The Lancet 395(10223), 497–506 (2020)

    Article  Google Scholar 

  6. Ahuja, S., Panigrahi, B.K., Dey, N., Rajinikanth, V., Gandhi, T.K.: Deep transfer learning-based automated detection of COVID-19 from lung CT scan slices. Appl. Intell. 51(1), 571–585 (2020). https://doi.org/10.1007/s10489-020-01826-w

    Article  Google Scholar 

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

    Article  Google Scholar 

  8. Kim, M., et al.: Deep learning in medical imaging. Neurospine 16(4), 657 (2019)

    Article  Google Scholar 

  9. Baltazar, L.R., et al.: Artificial intelligence on COVID-19 pneumonia detection using chest xray images. PLoS ONE 16(10), e0257884 (2021)

    Article  Google Scholar 

  10. Chung, M., et al.: CT imaging features of 2019 novel coronavirus (2019-nCoV). Radiology 295(1), 202–207, 26 (2020)

    Google Scholar 

  11. Wang, S., et al.: A deep learning algorithm using CT images to screen for corona virus disease (COVID-19). Eur. Radiol. 31(8), 6096–6104 (2021)

    Article  Google Scholar 

  12. Wang, W., Li, Y., Zou, T., Wang, X., You, J., Luo, Y.: A novel image classification approach via dense-MobileNet models. Mobile Information Systems (2020)

    Google Scholar 

  13. Cohen, J. P., Morrison, P., Dao, L.: COVID-19 image data collection (2020). arXiv preprint arXiv:2003.11597

  14. Alqudah, A.M., Qazan, S., Alqudah, A.: Automated systems for detection of COVID-19 using chest X-ray images and lightweight convolutional neural networks (2020)

    Google Scholar 

  15. Hall, L.O., Paul, R., Goldgof, D.B., Goldgof, G.M.: Finding covid-19 from chest x-rays using deep learning on a small dataset (2020). arXiv preprint arXiv:2004.02060

  16. Asraf, A., Islam, Z.: COVID19, Pneumonia and Normal Chest X-ray PA Dataset. Mendeley Data, V1 (2021). https://doi.org/10.17632/jctsfj2sfn.1

  17. Mangal, A., et al.: CovidAID: COVID-19 detection using chest X-ray (2020). arXiv preprint arXiv:2004.09803

  18. Luz, E., Silva, P.L., Silva, R., Silva, L., Moreira, G., Menotti, D.: Towards an effective and efficient deep learning model for covid19 patterns detection in x-ray images (2020). arXiv:2004.05717

  19. Hemdan, E.E.D., Shouman, M.A., Karar, M.E.: Covidx-net: a framework of deep learning classifiers to diagnose covid-19 in x ray images (2020). arXiv preprint arXiv:2003.11055

  20. Ilyas, M., Rehman, H., Nait-ali, A.: Detection of Covid-19 from chest X-ray images using artificial intelligence: an early review (2020). arXiv preprint arXiv:2004.05436

  21. Jacobi, A., Chung, M., Bernheim, A., Eber, C.: Portable chest X-ray in coronavirus disease-19 (COVID-19): a pictorial review. Clin. Imaging 64, 35–42 (2020)

    Article  Google Scholar 

  22. Tsai, E.B., et al.: The RSNA international COVID-19 open radiology database (RICORD). Radiology 299(1), E204–E213 (2021)

    Google Scholar 

  23. Zhang, R., et al.: COVID19XrayNet: a two-step transfer learning model for the COVID-19 detecting problem based on a limited number of chest X-Ray images. Interdiscip. Sci. Comput. Life Sci. 12(4), 555–565 (2020). https://doi.org/10.1007/s12539-020-00393-5

    Article  Google Scholar 

  24. Cohen, J.P., Morrison, P., Dao, L., Roth, K., Duong, T.Q., Ghassemi, M.: Covid-19 image data collection: prospective predictions are the future (2020). arXiv preprint arXiv:2006.11988

  25. Chowdhury, M.E.H., et al.: Can AI help in screening viral and COVID-19 pneumonia? IEEE Access 8, 132665–132676 (2020)

    Article  Google Scholar 

  26. Wang, R.: Edge detection using convolutional neural network. In: Cheng, L., Liu, Q., Ronzhin, A. (eds.) Advances in Neural Networks – ISNN 2016. LNCS, vol. 9719, pp. 12–20. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-40663-3_2

    Chapter  Google Scholar 

  27. Kermany, D.S., et al.: Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 172, 1122-1131.e9 (2018)

    Article  Google Scholar 

  28. Deng, R.O., et al.: ImageNet large scale visual recognition challenge (2015). arXiv preprint arXiv:14090575

  29. Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization (2014). arXiv preprint arXiv:1412.6980

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Kalyani Government Engineering College, Kalyani, India

    Arnab Dey

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  1. Arnab Dey
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Correspondence to Arnab Dey .

Editor information

Editors and Affiliations

  1. University of Vienna, Vienna, Austria

    Erich J. Neuhold

  2. Ryerson University, Toronto, ON, Canada

    Xavier Fernando

  3. University of Huddersfield, Huddersfield, UK

    Joan Lu

  4. University of Florida, Gainesville, FL, USA

    Selwyn Piramuthu

  5. SSN College of Engineering, Kalavakkam, India

    Aravindan Chandrabose

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Dey, A. (2022). COV-XDCNN: Deep Learning Model with External Filter for Detecting COVID-19 on Chest X-Rays. In: Neuhold, E.J., Fernando, X., Lu, J., Piramuthu, S., Chandrabose, A. (eds) Computer, Communication, and Signal Processing. ICCCSP 2022. IFIP Advances in Information and Communication Technology, vol 651. Springer, Cham. https://doi.org/10.1007/978-3-031-11633-9_14

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  • DOI: https://doi.org/10.1007/978-3-031-11633-9_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-11632-2

  • Online ISBN: 978-3-031-11633-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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