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COVID-19 Diagnosis Through Deep Learning Techniques and Chest X-Ray Images

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Abstract

The new coronavirus pandemic has brought disruption to the world. The lack of mass testing for the population is among the significant dilemmas to be solved by countries, especially underdeveloped ones. An alternative to deal with the lack of tests is detecting the disease by analyzing radiographic images. To process different types of images automatically, we employed deep learning algorithms to achieve success in recognizing different diagnostics. This work aims to train a deep learning model capable of automatically recognizing the COVID-19 diagnosis through radiographic images. Comparing images of coronavirus, healthy lung, and bacterial and viral pneumonia, we obtained a result with 93% accuracy.

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  1. https://www.who.int/news-room/detail/27-04-2020-who-timeline---covid-19.

  2. https://www.cdc.gov/coronavirus/2019-ncov/faq.html.

  3. https://www.worldometers.info/coronavirus/.

  4. https://www.worldometers.info/coronavirus/#countries.

  5. https://www.cdc.gov/coronavirus/2019-nCoV/index.html.

  6. https://www.who.int/emergencies/diseases/novel-coronavirus-2019/advice-for-public.

  7. https://www.elprocus.com/artificial-neural-networks-ann-and-their-types/.

  8. https://www.kaggle.com/pytorch/resnet34.

  9. http://image-net.org/.

  10. https://www.sirm.org/.

  11. https://research.google.com/colaboratory.

  12. https://github.com/ieee8023/covid-chestxray-dataset.

  13. https://data.mendeley.com/datasets/2fxz4px6d8/4.

  14. https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia.

  15. https://www.sirm.org/en/category/articles/covid-19-database/.

  16. https://docs.fast.ai/.

References

  1. Abbas A, Abdelsamea MM, Gaber MM. Classification of covid-19 in chest x-ray images using detrac deep convolutional neural network. 2020. arXiv:2003.13815.

  2. Aishwarya T, Ravi Kumar V. Machine learning and deep learning approaches to analyze and detect covid-19: a review. SN Comput Sci. 2021;2(3):1–9.

    Article  Google Scholar 

  3. Alom MZ, Rahman M, Nasrin MS, et al. Covid_mtnet: Covid-19 detection with multi-task deep learning approaches. 2020. arXiv:2004.03747.

  4. Altan A, Karasu S. Recognition of covid-19 disease from x-ray images by hybrid model consisting of 2d curvelet transform, chaotic salp swarm algorithm and deep learning technique. Chaos Solitons Fractals. 2020;140(110):071.

    MathSciNet  Google Scholar 

  5. Amyar A, Modzelewski R, Li H, et al. Multi-task deep learning based ct imaging analysis for covid-19 pneumonia: classification and segmentation. Comput Biol Med. 2020;126(104):037.

    Google Scholar 

  6. Apostolopoulos ID, Mpesiana TA. Covid-19: automatic detection from x-ray images utilizing transfer learning with convolutional neural networks. Phys Eng Sci Med. 2020;43(2):635–40.

    Article  Google Scholar 

  7. Bai HX, Hsieh B, Xiong Z, et al. Performance of radiologists in differentiating covid-19 from viral pneumonia on chest ct. Radiology. 2020;296(2):E46-54.

    Article  Google Scholar 

  8. Bello I, Zoph B, Vaswani A, et al. Attention augmented convolutional networks. In: Proceedings of the IEEE/CVF international conference on computer vision. 2019; p. 3286–95.

  9. Bernstein AV, Burnaev E, Kachan ON. Reinforcement learning for computer vision and robot navigation. In: International conference on machine learning and data mining in pattern recognition. Springer; 2018. p. 258–72.

  10. Canziani A, Paszke A, Culurciello E. An analysis of deep neural network models for practical applications. 2016. arXiv:1605.07678.

  11. Chassagnon G, Vakalopoulou M, Battistella E, et al. Ai-driven quantification, staging and outcome prediction of covid-19 pneumonia. Med Image Anal. 2021;67(101):860.

    Google Scholar 

  12. Chen H. Machine learning for information retrieval: neural networks, symbolic learning, and genetic algorithms. J Am Soc Inf Sci. 1995;46(3):194–216.

    Article  Google Scholar 

  13. Chieregato M, Frangiamore F, Morassi M, et al. A hybrid machine learning/deep learning covid-19 severity predictive model from ct images and clinical data. Sci Rep. 2022;12(1):1–15.

    Article  Google Scholar 

  14. Cohen JP, Dao L, Morrison P, et al. Predicting covid-19 pneumonia severity on chest x-ray with deep learning. Cureus. 2020.

  15. Coşkun M, Uçar A, Yildirim Ö, et al. Face recognition based on convolutional neural network. In: 2017 international conference on modern electrical and energy systems (MEES). IEEE; 2017. p. 376–379.

  16. Cui S, et al. Fish detection using deep learning. Appl Comput Intell Soft Comput. 2020. https://doi.org/10.1155/2020/3738108.

    Article  Google Scholar 

  17. Da Silva FL, Costa AHR. A survey on transfer learning for multiagent reinforcement learning systems. J Artif Intell Res. 2019;64:645–703.

    Article  MathSciNet  MATH  Google Scholar 

  18. de Oliveira RPdC, Sganderla GR, Maurício CRM, et al. Classificaçao de imagens de raio-x de torax com reconhecimento visual da ibm cloud para diagnostico de pneumonia. In: Anais Estendidos da XXXII Conference on graphics, patterns and images, SBC. 2019. p. 203–6.

  19. de Sousa OL, Magalhães DM, Vieira PdA, et al. Deep learning in image analysis for covid-19 diagnosis: a survey. IEEE Latin Am Trans. 2020;100(1e).

  20. dos Santos YCP, Estabelecidas C, Do Norte J. Desafios e impacto da inteligência artificial na medicina. 2017.

  21. Duarte KTN, Gobbi DG, Frayne R, et al. Detecting Alzheimer’s disease based on structural region analysis using a 3d shape descriptor. In: 2020 33rd SIBGRAPI conference on graphics, patterns and images (SIBGRAPI). 2020. p. 180–87. https://doi.org/10.1109/SIBGRAPI51738.2020.00032.

  22. Gorbalenya AE, Baker SC, Baric RS, et al. Coronaviridae study group of the international committee on taxonomy of viruses. The species severe acute respiratory syndrome-related coronavirus: classifying 2019-ncov and naming it sars-cov-2. Nat Microbiol. 2020;5(4):536–44.

    Article  Google Scholar 

  23. Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism. 2017;69:S36–40.

    Article  Google Scholar 

  24. Hu S, Gao Y, Niu Z, et al. Weakly supervised deep learning for covid-19 infection detection and classification from ct images. IEEE Access. 2020;8:118869–83.

    Article  Google Scholar 

  25. Hu T, Khishe M, Mohammadi M, et al. Real-time covid-19 diagnosis from x-ray images using deep cnn and extreme learning machines stabilized by chimp optimization algorithm. Biomed Signal Process Control. 2021;68(102):764.

    Google Scholar 

  26. Islam MM, Karray F, Alhajj R, et al. A review on deep learning techniques for the diagnosis of novel coronavirus (covid-19). IEEE Access. 2021;9:30,551-30,572. https://doi.org/10.1109/ACCESS.2021.3058537.

    Article  Google Scholar 

  27. Kim M, Kang J, Kim D, et al. Hi-covidnet: Deep learning approach to predict inbound covid-19 patients and case study in South Korea. In: Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining. association for computing machinery, New York, KDD ’20. 2020. p 3466–73. https://doi.org/10.1145/3394486.3412864.

  28. Ko H, Chung H, Kang WS, et al. Covid-19 pneumonia diagnosis using a simple 2d deep learning framework with a single chest ct image: model development and validation. J Med Internet Res. 2020;22(6):e19,569.

    Article  Google Scholar 

  29. Kopiler AA, et al. Redes neurais artificiais e suas aplicações no setor elétrico. Revista de Engenharias da Faculdade Salesiana. 2019;9:27–33.

    Google Scholar 

  30. Lau SLH, Wang X, Yang X, et al. Automated pavement crack segmentation using fully convolutional u-net with a pretrained resnet-34 encoder. IEEE Access. 2020.

  31. Lei L, Zhu H, Gong Y, et al. A deep residual networks classification algorithm of fetal heart ct images. In: 2018 IEEE international conference on imaging systems and techniques (IST). IEEE. 2018. p. 1–4.

  32. Li L, Qin L, Xu Z, et al. Artificial intelligence distinguishes covid-19 from community acquired pneumonia on chest ct. Radiology. 2020.

  33. Li S, Song W, Fang L, et al. Deep learning for hyperspectral image classification: an overview. IEEE Trans Geosci Remote Sens. 2019;57(9):6690–709.

    Article  Google Scholar 

  34. Lopez-Rincon A, Tonda A, Mendoza-Maldonado L, et al. Classification and specific primer design for accurate detection of sars-cov-2 using deep learning. Sci Rep. 2021;11(1):1–11.

    Article  Google Scholar 

  35. MAlnajjar MK, Abu-Naser SS. Heart sounds analysis and classification for cardiovascular diseases diagnosis using deep learning. IJARW. 2022.

  36. Negreiros RRB, dos Santos RA, Alves ALF, et al. Oil identification on beaches using deep learning techniques. In: Anais Estendidos do XXXIII conference on graphics, patterns and images, SBC. 2020. p. 167–70.

  37. Ohri K, Kumar M. Review on self-supervised image recognition using deep neural networks. Knowl-Based Syst. 2021;224(107):090.

    Google Scholar 

  38. Osóio FS, Bittencourt JR. Sistemas inteligentes baseados em redes neurais artificiais aplicados ao processamento de imagens. In: I Workshop de inteligência artificial. 2000.

  39. Ouyang W, Zeng X, Wang X, et al. Deepid-net: object detection with deformable part based convolutional neural networks. IEEE Trans Pattern Anal Mach Intell. 2016;39(7):1320–34.

    Article  Google Scholar 

  40. Phankokkruad M. Covid-19 pneumonia detection in chest x-ray images using transfer learning of convolutional neural networks. In: Proc. of the 3rd Intl. conf. on data science and information technology. Association for Computing Machinery, New York, DSIT 2020. 2020. p. 147–52.

  41. Rajaraman S, Siegelman J, Alderson PO, et al. Iteratively pruned deep learning ensembles for covid-19 detection in chest x-rays. IEEE Access. 2020.

  42. Rashed EA, Hirata A. Infectivity upsurge by covid-19 viral variants in Japan: evidence from deep learning modeling. Int J Environ Res Public Health. 2021;18(15):7799.

    Article  Google Scholar 

  43. Rodrigues JCL, et al. Performance of radiologists in differentiating covid-19 from viral pneumonia on chest ct. Public Health Emerg Collect. 2020. https://doi.org/10.1016/j.crad.2020.03.003.

  44. Shi F, Wang J, et al. Review of artificial intelligence techniques in imaging data acquisition, segmentation and diagnosis for covid-19. IEEE Rev Biomed Eng. 2020.

  45. Shiaelis N, Tometzki A, Peto L, et al. Virus detection and identification in minutes using single-particle imaging and deep learning. MedRxiv. 2022. p. 2020–10.

  46. Shorten C, Khoshgoftaar TM, Furht B. Deep learning applications for covid-19. J Big Data. 2021;8(1):1–54.

    Article  Google Scholar 

  47. Silva I, Leoni G, Sadok D, et al. Classifying covid-19 positive x-ray using deep learning models. IEEE Lat Am Trans. 2021;19:884–92. https://doi.org/10.1109/TLA.2021.9451232.

    Article  Google Scholar 

  48. Silva. I, Negreiros. R, Alves. A, et al. Classification of chest x-ray images to diagnose covid-19 using deep learning techniques. In: Proceedings of the 19th international conference on wireless networks and mobile systems—WINSYS,, INSTICC. SciTePress; 2022. p. 93–100. https://doi.org/10.5220/0011339700003286.

  49. Singh S, Ahuja U, Kumar M, et al. Face mask detection using yolov3 and faster r-cnn models: Covid-19 environment. Multimed Tools Appl. 2021;80(13):19,753-19,768.

    Article  Google Scholar 

  50. Spörl C, Castro E, Luchiari A. Aplicação de redes neurais artificiais na construção de modelos de fragilidade ambiental. Revista do Departamento de Geografia. 2011;21:113–35.

    Google Scholar 

  51. Topol EJ. Welcoming new guidelines for ai clinical research. Nat Med. 2020;26(9):1318–20.

    Article  Google Scholar 

  52. Voulodimos A, Doulamis N, Doulamis A, et al. (2018) Deep learning for computer vision: a brief review. Comput Intell Neurosci. 2018;7068:349.

    Google Scholar 

  53. Wong HYF, Lam HYS, Fong AH, et al. Frequency and distribution of chest radiographic findings in patients positive for covid-19. Radiology. 2020. https://doi.org/10.1148/radiol.2020201160.

  54. Wu X, Hui H, Niu M, et al. Deep learning-based multi-view fusion model for screening 2019 novel coronavirus pneumonia: a multicentre study. Eur J Radiol. 2020;128(109):041.

    Google Scholar 

  55. Wynants L, Van Calster B, Collins GS, et al. Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal. BMJ. 2020;369.

  56. Zhao ZQ, Zheng P, St Xu, et al. Object detection with deep learning: a review. IEEE Trans Neural Netw Learn Syst. 2019;30(11):3212–32.

    Article  Google Scholar 

  57. Zheng C, Deng X, Fu Q, et al. Deep learning-based detection for covid-19 from chest ct using weak label. IEEE Trans Med Imaging. 2020.

  58. Zhu X, Goldberg AB. Introduction to semi-supervised learning. Synth Lect Artif Intell Mach Learn. 2009;3(1):1–130.

    MATH  Google Scholar 

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Correspondence to André Luiz Firmino Alves.

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Negreiros, R.R.B., Silva, I.H.S., Alves, A.L.F. et al. COVID-19 Diagnosis Through Deep Learning Techniques and Chest X-Ray Images. SN COMPUT. SCI. 4, 613 (2023). https://doi.org/10.1007/s42979-023-02043-1

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