Skip to main content

Advertisement

Springer Nature Link
Log in

Online diagnosis of COVID-19 from chest radiography images by using deep learning algorithms

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

The COVID-19 outbreak, which has a devastating impact on the health and well-being of the global population, is a respiratory disease. It is vital to determine, isolate and treat people with the disease as soon as possible to fight against the COVID-19 pandemic. Even though the reverse transcription polymerase chain reaction (RT-PCR) test, the accuracy of which is about 63%, seems to be a good option for determining COVID-19, it is a disadvantage is that test kits are few, are difficult to obtain in remote rural areas and have low accuracy. Chest X-ray (CXR) has become essential for rapidly diagnosing the rapidly spreading COVID-19 disease worldwide, so it is urgent to develop an online system that will help specialists identify infected patients with CXR images. In this study developed a transfer learning-based diagnosis system for online diagnosis of COVID-19 patients using CXR images. Transfer learning-based deep learning models VGG16, VGG19, ResNet50, InceptionV3, Xception, MobileNet, DenseNet121 and DenseNet201 were used for the experimental studies. We explored the COVID-19 radiography database from Kaggle, which is open to the public, using image preprocessing techniques and data augmentation. The images captured by the various terminals are transferred to the web server in the created system. Similar to the ensemble learning approach, the percentage accuracy of the model with the highest prediction value among the eight deep learning models is displayed on the screen. The results show that the proposed online diagnosis system performs better than others with the highest accuracy, precision, recall and F1 values of 98%, 99%, 97% and 97%, respectively. The results show that deep learning models help to increase the efficiency of chest radiograph scanning and have promising potential in predicting COVID-19 cases. The online diagnostic system will be a helpful tool for radiologists as it diagnoses COVID-19 quickly and with high accuracy.

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
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Asif S, Wenhui Y, Jin H, Jinhai S (2020) Classification of COVID-19 from Chest X-ray images using deep convolutional neural network. In: 2020 IEEE 6th international conference on computer and communications (ICCC), pp 426–433

  2. Alhudhaif A, Polat K, Karaman O (2021) Determination of COVID-19 pneumonia based on generalized convolutional neural network model from chest X-ray images. Expert Syst Appl 180:115141

    Article  Google Scholar 

  3. https://www.who.int/data/. World Health Organization Accessed 22 July 2023

  4. Ko H, Chung H, Kang WS, Kim KW, Shin Y, Kang SJ, Lee JH, Kim YJ, Kim NY, Jung H, Lee J (2020) 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 22(6):e19569

    Article  Google Scholar 

  5. Bleve G, Rizzotti L, Dellaglio F, Torriani S (2003) Development of reverse transcription (RT)-PCR and real-time RT-PCR assays for rapid detection and quantification of viable yeasts and molds contaminating yogurts and pasteurized food products. Appl Environ Microbiol 69(7):4116–4122

    Article  Google Scholar 

  6. Long C, Xu H, Shen Q, Zhang X, Fan B, Wang C, Zeng B, Li Z, Li X, Li H (2020) Diagnosis of the Coronavirus disease (COVID-19): rRT-PCR or CT? Eur J Radiol 126:108961

    Article  Google Scholar 

  7. Sahinbas, K., & Catak, F. O. (2021). Transfer learning-based convolutional neural network for COVID-19 detection with X-ray images. In: Data science for COVID-19, Academic Press, pp 451–466

  8. Karaman O (2021) Boosting performance of transfer learning model for diagnosis of COVID-19 from computer tomography scans. Süleyman Demirel Üniversitesi Fen Edebiyat Fakültesi Fen Dergisi 16(1):35–45

    Article  Google Scholar 

  9. Greenspan H, Van Ginneken B, Summers RM (2016) Guest editorial deep learning in medical imaging: overview and future promise of an exciting new technique. IEEE Trans Med Imaging 35(5):1153–1159

    Article  Google Scholar 

  10. Karaman O, Alhudhaif A, Polat K (2021) Development of smart camera systems based on artificial intelligence network for social distance detection to fight against COVID-19. Appl Soft Comput 110:107610

    Article  Google Scholar 

  11. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 25:1097–1105

    Google Scholar 

  12. Tatli U, Budak C (2023) Biomedical image segmentation with modified U-Net. Traitement du Sig 40(2):523–531. https://doi.org/10.18280/ts.400211

    Article  Google Scholar 

  13. Budak C, Mençik V (2022) Detection of ring cell cancer in histopathological images with region of interest determined by SLIC superpixels method. Neural Comput Appl 34(16):13499–13512

    Article  Google Scholar 

  14. Jiang X, Coffee M, Bari A, Wang J, Jiang X, Huang J, Shi J, Dai J, Cai J, Zhang T, Wu Z, He G, Huang Y (2020) Towards an artificial intelligence framework for data-driven prediction of coronavirus clinical severity. Comput Mater Contin 63(1):537–551

    Google Scholar 

  15. de Moraes Batista AF, Miraglia JL, Donato THR, Chiavegatto Filho ADP (2020) COVID-19 diagnosis prediction in emergency care patients: a machine learning approach. medRxiv.

  16. Wang S, Kang B, Ma J, Zeng X, Xiao M, Guo J, Cai M, Yang J, Li Y, Meng X, Xu B (2021) A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19). Euro Radiol 31:6096–6104

    Article  Google Scholar 

  17. Bhandary A, Prabhu GA, Rajinikanth V, Thanaraj KP, Satapathy SC, Robbins DE, Shasky C, Zang Y-D, Tavares JMRS, Raja NSM (2020) Deep-learning framework to detect lung abnormality–a study with chest X-Ray and lung CT scan images. Pattern Recogn Lett 129:271–278

    Article  Google Scholar 

  18. Apostolopoulos ID, Mpesiana TA (2020) Covid-19: automatic detection from x-ray images utilizing transfer learning with convolutional neural networks. Phys Eng Sci Med 43(2):635–640

    Article  Google Scholar 

  19. Hemdan EED, Shouman MA, Karar ME (2020). Covidx-net: a framework of deep learning classifiers to diagnose covid-19 in x-ray images. arXiv:2003.11055

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

  21. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp770–778

  22. Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2818–2826

  23. Chollet F (2017) Xception: deep learning with depthwise separable convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1251–1258.

  24. Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H (2017) Mobilenets: efficient convolutional neural networks for mobile vision applications. arXiv:1704.04861

  25. Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition p 4700–4708

  26. Rahman T, Chowdhury M, Khandakar A, (2021) COVID-19 radiography database. https://www.kaggle.com/tawsifurrahman/covid19-radiography-database

  27. Albawi S, Mohammed TA, Al-Zawi S (2017) Understanding of a convolutional neural network. In: 2017 International conference on engineering and technology (ICET) p 1–6

  28. Stark JA (2000) Adaptive image contrast enhancement using generalizations of histogram equalization. IEEE Trans Image Process 9(5):889–896

    Article  Google Scholar 

  29. Shorten C, Khoshgoftaar TM (2019) A survey on image data augmentation for deep learning. J Big Data 6(1):1–48

    Article  Google Scholar 

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

    Article  Google Scholar 

  31. Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. The Jo Mach Learn Res 15(1):1929–1958

    MathSciNet  MATH  Google Scholar 

  32. Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International conference on machine learning, p 448–456. PMLR.

  33. Keras: the Python deep learning API. https://keras.io/. Accessed 1 Feb 2023

  34. Tensorflow Transfer Learning. https://www.tensorflow.org/tutorials/images/transfer_learning.

  35. Toğaçar M, Ergen B, Cömert Z (2020) COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches. Comput Biol Med 121:103805

    Article  Google Scholar 

  36. 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 121:103792

    Article  Google Scholar 

  37. Ucar F, Korkmaz D (2020) COVIDiagnosis-Net: Deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images. Med Hypotheses 140:109761

    Article  Google Scholar 

  38. El Asnaoui K, Chawki Y, Idri A (2021) Automated methods for detection and classification pneumonia based on x-ray images using deep learning. In: Maleh Y, Baddi Y, Alazab M, Tawalbeh L, Romdhani I (eds) Artificial intelligence and blockchain for future cybersecurity applications. Springer, Cham, pp 257–284

    Chapter  Google Scholar 

  39. Polat Ç, Karaman O, Karaman C, Korkmaz G, Balcí MC, Kelek SE (2021) COVID-19 diagnosis from chest X-ray images using transfer learning: enhanced performance by debiasing dataloader. J X-ray Sci Technol 29(1):19–3

    Google Scholar 

  40. Zhang J, Xie Y, Pang G, Liao Z, Verjans J, Li W, Sun Z, He J, Li Y, Shen C, Xia Y (2020) Viral pneumonia screening on chest X-ray images using confidence-aware anomaly detection. arXiv:2003.12338

  41. Rahimzadeh M, Attar A (2020) A new modified deep convolutional neural network for detecting COVID-19 from X-ray images. arXiv e-prints, arXiv-2004.

  42. Wang L, Lin ZQ, Wong A (2020) Covid-net: a tailored deep convolutional neural network design for detection of covid-19 cases from chest x-ray images. Sci Rep 10(1):1–12

    Google Scholar 

  43. El Asnaoui K, Chawki Y (2020) Using X-ray images and deep learning for automated detection of coronavirus disease. J Biomol Struct Dyn 39:3615–3626

    Article  Google Scholar 

  44. 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

  45. Sethy PK, Behera SK (2020) Detection of coronavirus disease (covid-19) based on deep features. Preprints 2020:2020030300

    Google Scholar 

  46. Bhoumik S, Chatterjee S, Sarkar A, Kumar A, John Joseph FJ (2020) Covid 19 Prediction from X Ray images using fully connected convolutional neural network. In: CSBio’20: proceedings of the eleventh international conference on computational systems-biology and bioinformatics p 106–107

Download references

Author information

Authors and Affiliations

  1. Department of Electric-Electronic Engineering, Dicle University, Diyarbakır, Turkey

    Cafer Budak, Vasfiye Mençik & Osman Varışlı

Authors
  1. Cafer Budak
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Vasfiye Mençik
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Osman Varışlı
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Cafer Budak.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Budak, C., Mençik, V. & Varışlı, O. Online diagnosis of COVID-19 from chest radiography images by using deep learning algorithms. Neural Comput & Applic 35, 20717–20734 (2023). https://doi.org/10.1007/s00521-023-08867-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-023-08867-5

Keywords