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Deep learning framework for early detection of COVID-19 using X-ray images

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Abstract

The whole world is imposing efforts to combat the deadly COVID-19 virus that continues to have a disastrous effect on health, economy, education, transport & communication, and many other sectors. The crucial action taken to control its rapid spread is first to detect the infected person. Deep learning-based algorithms utilize mathematical models to detect Covid-19 cases. Deep learning approach is applied to track and diagnose Covid-19 and help radiologists and medical doctors enhance prognosis performance. X-ray images are popularly used deep learning methods for Covid-19 detection. However, the existing techniques suffer from several limitations that need to be addressed to detect Covid-19 cases more accurately: Firstly, there is a small number of Covid-19 images. Secondly, an unbalanced dataset. Thirdly, model overfitting, and fourthly, correct detection of Covid-19 and pneumonia cases sometimes does not provide accurate results because COVID-19 and pneumonia symptoms are similar. Therefore, this paper aimed to develop an automated solution to classify the detected Covid-19 into two classes to overcome the small and unbalanced dataset, and model overfitting problems. This study compared nine state-of-the-art CNN architectures through a transfer learning approach. Our approach achieved better results in comparison to the work done on this benchmark dataset yielding 99.86% accuracy with 99.9% recall using VGG-16 using deep learning model. The proposed framework presents a transfer learning technique to increase the performance of the deep learning-based Covid-19 detection method. Moreover, the Comparative evaluation presents that the proposed framework outperforms existing methods. Close results show that VGG-16 on a large dataset correctly identified COVID-19.

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Data sharing not applicable to this article as no publicly available datasets were generated or analyzed during the current study.

References

  1. Alimadadi A, Aryal S, Manandhar I, Munroe PB, Joe B, Cheng X (2020) Artificial intelligence and machine learning to fight covid-19. Physiol Genomics 52(4):200–202. https://doi.org/10.1152/physiolgenomics.00029.2020

    Article  Google Scholar 

  2. Apostolopoulos ID, Mpesiana TA (2020) Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks. Australas Phys Eng Sci Med 43(2):635–640. https://doi.org/10.1007/s13246-020-00865-4

    Article  Google Scholar 

  3. Barstugan M, Ozkaya U, Ozturk S (2020) Coronavirus (COVID-19) classification using CT images by machine learning methods. arXiv 5:1–10

    Google Scholar 

  4. Brunese L, Mercaldo F, Reginelli A, Santone A (2020) Explainable deep learning for pulmonary disease and coronavirus COVID-19 detection from X-rays. Comput Methods Prog Biomed 196:105608. https://doi.org/10.1016/j.cmpb.2020.105608

    Article  Google Scholar 

  5. Carbonneau M-A, Cheplygina V, Granger E, Gagnon G (2018) Multiple instance learning: a survey of problem characteristics and applications. Pattern Recognit 77:329–353. https://doi.org/10.1016/j.patcog.2017.10.009

  6. Cohen, JP (2020) Open database of covid-19 cases, https://github.com/ieee8023/covid-chestxray-dataset. Accessed 15 Oct 2021

  7. Farid AA, Selim GI, Khater HAA (2020) A novel approach of CT images feature analysis and prediction to screen for corona virus disease (COVID-19). Int J Sci Eng Res 11(03):1141–1149. https://doi.org/10.14299/ijser.2020.03.02

    Article  Google Scholar 

  8. Ghoshal B, Tucker, A. (2020) Estimating uncertainty and interpretability in deep learning for coronavirus (COVID-19) detection. 1–14. http://arxiv.org/abs/2003.10769

  9. Gozes O et al. (2020) Rapid AI development cycle for the coronavirus (COVID-19) Pandemic: Initial results for automated detection & patient monitoring using deep learning CT image analysis, [Online]. Available: http://arxiv.org/abs/2003.05037. Accessed 15 Oct 2021

  10. Gunraj H, Wang L, Wong A (2020) COVIDNet-CT: A tailored deep convolutional neural network design for detection of COVID-19 Cases from Chest CT Images, pp. 1–12, [Online]. Available: http://arxiv.org/abs/2009.05383. Accessed 15 Oct 2021

  11. Hall LO, Paul R, Goldgof DB, Goldgof GM (2020) Finding Covid-19 from chest x-rays using deep learning on a small dataset, pp. 1–8, [Online]. Available: http://arxiv.org/abs/2004.02060. Accessed 15 Oct 2021

  12. Han Z et al (2020) Accurate screening of COVID-19 using attention-based deep 3D multiple instance learning. IEEE Trans Med Imaging 39(8):2584–2594. https://doi.org/10.1109/TMI.2020.2996256

    Article  Google Scholar 

  13. Hasan MJ, Alom MS, Ali MS (2021) Deep learning based detection and segmentation of COVID-19 & pneumonia on chest x-ray image. In: 2021 international conference on information and communication technology for sustainable development (ICICT4SD), Dhaka, Bangladesh, pp 210–214. https://doi.org/10.1109/ICICT4SD50815.2021.9396878

  14. Islam MZ, Islam MM, Asraf A (2020) A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images. Inf Med Unlocked 20:100412. https://doi.org/10.1016/j.imu.2020.100412

    Article  Google Scholar 

  15. Kassania SH, Kassasni PH, Wesolowski MJ, Schneider KA, Deters R (2020) Automatic detection of coronavirus disease (COVID-19) in X-ray and CT images: A machine learning based approach, arXiv, vol. 2019, pp. 1–18

  16. Liao L, Li H, Shang W, Ma L (2022) An empirical study of the impact of Hyperparameter tuning and model optimization on the performance properties of deep neural networks. ACM trans. Softw. Eng. Methodol. 31, 3, article 53 (July 2022), 40 pages. https://doi.org/10.1145/3506695

  17. Mooney P (2018) Chest X-Ray Images (Pneumonia), https://www.kaggle.com/paultimothymooney/chest-xraypneumonia. Accessed 15 Oct 2021

  18. Narin, A, Kaya, C, Pamuk, Z (2020) Department of Biomedical Engineering, Zonguldak Bulent Ecevit University, 67100, Zonguldak, Turkey. ArXiv Preprint ArXiv:2003.10849. https://arxiv.org/abs/2003.10849. Accessed 15 Oct 2021

  19. Nour, M, Cömert, Z, Polat, K (2020) A novel medical diagnosis model for COVID-19 infection detection based on deep features and Bayesian optimization. Appl Soft Comput J xxxx, 106580. https://doi.org/10.1016/j.asoc.2020.106580

  20. Oh Y, Park S, Ye JC (2020) Deep learning COVID-19 features on CXR using limited training data sets. arXiv 39(8):2688–2700

    Google Scholar 

  21. Ozturk, T, Talo, M, Azra, E, Baran, U, Yildirim, O (2020) Since January 2020 Elsevier has created a COVID-19 resource Centre with free information in English and mandarin on the novel coronavirus COVID- 19 . The COVID-19 resource centre is hosted on Elsevier Connect , the company ’ s public news and information. Computers in Biology and Medicine, January

  22. Rahimzadeh M, Attar A (2020) A modified deep convolutional neural network for detecting COVID-19 and pneumonia from chest X-ray images based on the concatenation of Xception and ResNet50V2. Inf Med Unlocked 19:100360. https://doi.org/10.1016/j.imu.2020.100360

    Article  Google Scholar 

  23. Saha P, Sadi MS, Islam MM (2021) EMCNet: automated COVID-19 diagnosis from X-ray images using convolutional neural network and ensemble of machine learning classifiers. Inf Med Unlocked 22:100505. https://doi.org/10.1016/j.imu.2020.100505

    Article  Google Scholar 

  24. Sethy PK, Behera SK, Ratha PK, Biswas P (2020) Detection of coronavirus disease (COVID-19) based on deep features and support vector machine. Int J Math Eng Manag Sci 5(4):643–651. https://doi.org/10.33889/IJMEMS.2020.5.4.052

    Article  Google Scholar 

  25. Singh D, Kumar V, Vaishali, Kaur M (2020) Classification of COVID-19 patients from chest CT images using multi-objective differential evolution–based convolutional neural networks. Eur J Clin Microbiol Infect Dis 39(7):1379–1389. https://doi.org/10.1007/s10096-020-03901-z

    Article  Google Scholar 

  26. Ucara F, Korkmaz D (2020) Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID- 19 . The COVID-19 resource centre is hosted on Elsevier Connect , the company ’ s public news and information , January

  27. Wang X, Peng Y, Lu L, Lu Z, Bagheri M, Summers RM (2017) ChestX-Ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In: 2017 IEEE Conference on computer vision and pattern recognition (CVPR), Honolulu, HI, USA, pp 3462–3471. https://doi.org/10.1109/CVPR.2017.369

  28. Wu J, Chen X-Y, Zhang H, Xiong L-D, Lei H, Deng S-H (2019) Hyperparameter optimization for machine learning models based on Bayesian Optimization. J Electron Sci Technol 17(1):26–40. https://doi.org/10.11989/JEST.1674-862X.80904120

    Article  Google Scholar 

  29. Yang L, Shami A (2020) On hyperparameter optimization of machine learning algorithms: theory and practice. Neurocomputing 415:295–316, ISSN 0925-2312. https://doi.org/10.1016/j.neucom.2020.07.061

    Article  Google Scholar 

  30. Zhong L, Gong P, Biging GS (2012) Correlation of Chest CT and RT-PCR Testing in Coronavirus Disease 2019 (COVID-19) in China: A Report of 1014 Cases 78, no. May, pp. 1–15

  31. Zhou T, Li L, Bredell G, Li J, Unkelbach J, Konukoglu E (2023) Volumetric memory network for interactive medical image segmentation, Medical Image Analysis, Volume 83, 102599, ISSN 1361-8415, https://doi.org/10.1016/j.media.2022.102599

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Authors and Affiliations

  1. Department of Computer Science, Shaheed Zulfikar Ali Bhutto Institute of Science and Technology (SZABIST), Islamabad Campus, Street # 09, Plot # 67, Sector H-8/4, Islamabad, Pakistan

    Kainat Khero & Muhammad Usman

  2. Department of Computer Science, Western Michigan University, 1903 W. Michigan Ave, Kalamazoo, MI, 49008, USA

    Alvis Fong

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  1. Kainat Khero
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  2. Muhammad Usman
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Correspondence to Alvis Fong.

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Khero, K., Usman, M. & Fong, A. Deep learning framework for early detection of COVID-19 using X-ray images. Multimed Tools Appl 83, 6883–6908 (2024). https://doi.org/10.1007/s11042-023-15995-6

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