Top

Published in:

2022 | OriginalPaper | Chapter

Convolutional Neural Network-Based Approach to Detect COVID-19 from Chest X-Ray Images

Authors : P. Pandiaraja, K. Muthumanickam

Published in: Cyber Security, Privacy and Networking

Publisher: Springer Nature Singapore

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

COVID-19 is a worldwide pandemic that poses serious health hazards. COVID-19’s diagnostic test sensitivity is restricted owing to specimen processing abnormalities. The discussed technique might be used in clinical practice as a computer-aided diagnostics approach for COVID-19. The use of chest X-ray pictures for detection is life-saving for both patients and clinicians. Furthermore, in nations where laboratory kits for testing are unavailable, this becomes even more critical. This work aims to demonstrate the application of deep learning for high-accuracy COVID-19 identification utilizing chest X-ray images. Image-based applications have reached a pinnacle in the last five years thanks to the widespread usage of convolutional neural networks (CNNs). CNN gathers information from images by extracting features. The enormous popularity and efficacy of CNNs have sparked a new rise in interest in deep learning. The image data space is littered with CNN models. They excel in computer vision tasks like image categorization, object identification, and image recognition. This research work attempts to discuss the CNN-based approach for detecting COVID-19 from chest X-ray images.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

previous chapter Toward Big Data Various Challenges and Trending Applications

next chapter Classification of Medical Health Records Using Convolutional Neural Networks for Optimal Diagnosis

Ozturk T, Talo M, Yildirim EA, Baloglu UB, Yildirim O, Acharya UR (2020) Automated detection of COVID-19 cases using deep neural networks with X-ray images. Comput Biol Med 103792. PMID: 32568675

Lee EY, Ng MY, Khong PL (2020) COVID-19 pneumonia: what has CT taught us? Lancet Infect Dis 20(3):384–385

Pandiaraja P, Sharmila S (2020) Optimal routing path for heterogeneous vehicular adhoc network. Int J Adv Sci Technol 29(7s):1762–1771

He K, Zhang X, Ren S, Sun J (2016) Identity mappings in deep residual networks. In: European conference on computer vision, pp 630–645

Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556

Zheng C, Deng X, Fu Q et al (2020) Deep learning-based detection for COVID-19 from chest CT using weak label. medRxiv

Majeed T, Rashid R, Ali D, Asaad A (2020) Covid-19 detection using CNN transfer learning from X-ray images. medRxiv

Xu X, Jiang X, Ma C, Du P, Li X, Lv S, Yu L, Chen Y, Su J, Lang G, Li Y, Zhao H, Xu K, Ruan L, Wu W (2020) Deep learning system to screen coronavirus disease 2019 pneumonia. arXiv:2002.09334

Deepika S, Pandiaraja P (2013) Ensuring CIA triad for user data using collaborative filtering mechanism. In: 2013 international conference on information communication and embedded systems (ICICES), pp 925-928. https://doi.org/10.1109/ICICES.2013.6508262

10.

El Asnaoui K, Chawki Y (2020) Using X-ray images and deep learning for automated detection of coronavirus disease. J Biomol Struct Dyn 1–12

11.

Deepa N, Pandiaraja P (2020) Hybrid context aware recommendation system for E-health care by merkle hash tree from cloud using evolutionary algorithm. Soft Comput 24:7149–7161CrossRef

12.

Chollet F (2020) Xception: deep learning with depth wise separable convolutions. In: 2017 IEEE CVPR, pp 1251–1258

13.

Minaee S, Kafieh R, Sonka M, Yazdani S, Soufi GJ (2020) Deep-covid: predicting covid-19 from chest X-ray images using deep transfer learning. arXiv preprint arXiv:2004.09363

14.

Koo HJ, Lim S, Choe J, Choi SH, Sung H, Do KH (2018) Radiographic and CT features of viral pneumonia. Radiographics 38(3):719–739

15.

Pandiaraja P, SanthanaHari S, Suriya S, Karthikeyan S (2020) Convolutional neural network for solid waste segregation and management. Int J Adv Sci Technol 29(7s):1661–1668

16.

Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, Zhang L, Fan G, Xu J, Gu X, Cheng Z (2020) Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 395(10223):497–506CrossRef

17.

Holshue ML, DeBolt C (2020) First case of 2019 novel coronavirus in the United States. N Engl J Med 328:929–936CrossRef

18.

Narin A, Kaya C, Pamuk Z (2020) Automatic detection of coronavirus disease (covid-19) using X-ray images and deep convolutional neural networks. arXiv preprint arXiv:2003.10849

19.

Pandiaraja P, Aravinthan K, Lakshmi Narayanan R, Kaaviya KS, Madumithra K (2020) Efficient cloud storage using data partition and time based access control with secure AES encryption technique. Int J Adv Sci Technol 29(7s):1698–1706

20.

Sumathi K, Pandiaraja P (2020) Dynamic alternate buffer switching and congestion control in wireless multimedia sensor networks. Peer-to-Peer Netw Appl 13:2001–2010CrossRef

21.

Zhang Y et al (2020) COVID-DA: deep domain adaptation from typical pneumonia to COVID-19. arXiv:2005.01577

22.

Pandiaraja P, Manikandan J (2015) Web proxy based detection and protection mechanisms against client based HTTP attacks. In: 2015 international conference on circuits, power and computing technologies [ICCPCT-2015], pp 1–6. https://doi.org/10.1109/ICCPCT.2015.7159344

23.

Yoon SH, Lee KH (2020) Chest radiographic and CT findings of the 2019 novel coronavirus disease (COVID-19): analysis of nine patients treated in Korea. Korean J Radiol

24.

Deng J et al (2009) Imagenet: a large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition, pp 248–255

25.

Huang G, Liu Z, Weinberger KQ (2016) Densely connected convolutional networks. arXiv preprint arXiv:1608.06993

26.

Deepa N, Pandiaraja P (2021) E health care data privacy preserving efficient file retrieval from the cloud service provider using attribute based file encryption. J Ambient Intell Human Comput 12:4877–488CrossRef

27.

Khobahi S, Agarwal C, Soltanalian M (2020) Coronet: a deep network architecture for semi-supervised task-based identification of covid-19 from chest X-ray images. medRxiv. https://doi.org/10.1101/2020.04.14.20065722

28.

Shen D, Wu G, Suk HI (2017) Deep learning in medical image analysis. Annu Rev Biomed Eng 19:221–248CrossRef

29.

Rajpurkar P, Irvin J (2017) Chexnet: radiologist-level pneumonia detection on chest X-rays with deep learning. arXiv preprint arXiv:1711.05225

30.

Luz E et al (2020) Towards an effective and efficient deep learning model for covid-19 patterns detection in X-ray images. arXiv:2004.05717

31.

LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444CrossRef

32.

Rasheed J, Jamil A, Hameed AA, Aftab U, Aftab J, Shah SA et al (2020) A survey on artificial intelligence approaches in supporting frontline workers and decision makers for COVID-19 pandemic. Chaos Solitons Fractals 110337. PMID: 33071481

33.

Pandiaraja P, Deepa N (2019) A novel data privacy-preserving protocol for multi-data users by using genetic algorithm. Soft Comput 23:8539–8553CrossRef

34.

Zu ZY, Jiang MD, Xu PP, Chen W, Ni QQ, Lu GM, Zhang LJ (2020) Coronavirus disease 2019 (COVID-19): a perspective from China. Radiology. https://doi.org/10.1148/radiol.2020200490

35.

Kong W, Agarwal PP (2020) Chest imaging appearance of COVID-19 infection. Radiol Cardiothorac Imaging

36.

Li Y, Xia L (2020) Coronavirus disease 2019 (COVID-19): role of chest CT in diagnosis and management. Am J Roentgenol

37.

Pandiaraja P, Parasuraman S (2015) Applying secure authentication scheme to protect DNS from rebinding attack using proxy. In: 2015 international conference on circuits, power and computing technologies [ICCPCT-2015], pp 1–6. https://doi.org/10.1109/ICCPCT.2015.7159255

38.

Zheng C, Deng X, Fu Q, Zhou Q, Feng J, Ma H, Wang X (2020) Deep learning-based detection for COVID-19 from chest CT using weak label. medRxiv. https://doi.org/10.1101/2020.03.12.20027185

39.

Barstugan M, Ozkaya U, Ozturk S (2020) Coronavirus (COVID-19) classification using CT images by machine learning methods. arXiv preprint arXiv:2003.09424

40.

Gaal G, Maga B, Lukács A (2020) Attention U-Net based adversarial architectures for chest X-ray lung segmentation. arXiv preprint arXiv:2003.10304

41.

Pan SJ, Yang Q (2009) A survey on transfer learning. IEEE Trans Knowl Data Eng 22(10):1345–1359

42.

Wang S, Kang B, Ma J et al (2020) A deep learning algorithm using CT images to screen for corona virus disease (COVID-19). medRxiv

43.

Chouhan V, Singh SK, Khamparia A et al (2020) A novel transfer learning based approach for pneumonia detection in chest X-ray images. Appl Sci 10(2):559CrossRef

44.

Faust O, Hagiwara Y, Hong TJ, Lih OS, Acharya UR (2018) Deep learning for healthcare applications based on physiological signals: a review. Comput Methods Programs Biomed 161:1–13. PMID: 29852952

45.

Osman AH, Aljahdali HMA (2020) An effective of ensemble boosting learning method for breast cancer virtual screening using neural network model. IEEE Access 8:39165–39174CrossRef

46.

Sedik A, Hammad M, Abd El-Samie FE, Gupta BB, Abd El-Latif AA (2021) Efficient deep learning approach for augmented detection of coronavirus disease. Neural Comput Appl 1–18. PMID: 33487885

47.

Kamal K, Yin Z, Wu M, Wu Z (2021) Evaluation of deep learning-based approaches for COVID-19 classification based on chest X-ray images. Signal Image Video Process 1–8

48.

Singh RK, Pandey R, Babu RN (2021) COVIDScreen: explainable deep learning framework for differential diagnosis of COVID-19 using chest X-rays. Neural Comput Appl 1–22. PMID: 33437132

49.

Talo M, Yildirim O, Baloglu UB, Aydin G, Acharya UR (2019) Convolutional neural networks for multi-class brain disease detection using MRI images. Comput Med Imaging Graph 78:101673. PMID: 31635910

Title: Convolutional Neural Network-Based Approach to Detect COVID-19 from Chest X-Ray Images
Authors: P. Pandiaraja
K. Muthumanickam
Publisher: Springer Nature Singapore
Book: Cyber Security, Privacy and Networking
Print ISBN: 978-981-16-8663-4

Electronic ISBN: 978-981-16-8664-1

Copyright Year: 2022
DOI: https://doi.org/10.1007/978-981-16-8664-1_21

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"