Skip to main content
Springer Nature Link
Log in

Deep Learning for Healthcare: A Web-Microservices System Ready for Chest Pathology Detection

  • Conference paper
  • First Online:
Good Practices and New Perspectives in Information Systems and Technologies (WorldCIST 2024)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 986))

Included in the following conference series:

  • 133 Accesses

Abstract

The automation of medical diagnosis has accelerated thanks to the integration of artificial intelligence (AI), particularly in interpreting pathologies in chest X-rays. This study presents a web microservices system that uses a deep learning model to classify thoracic pathologies. The system improves clinical decision-making by providing visual aids, including heat maps for model explainability and a comprehensive set of medical image manipulation tools. The back-end, developed using a microservices architecture, ensures robust data management, secure user authentication, and efficient AI model integration. The results highlight the system’s accuracy in detecting pathologies with an average AUC of 0.89, an easy-to-use interface, and the transformative impact of AI explainability in clinical settings.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

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

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    https://angular.io/.

  2. 2.

    https://www.w3.org/TR/2014/REC-html5-20141028/.

  3. 3.

    https://www.w3.org/Style/CSS/Overview.en.html.

  4. 4.

    https://flask.palletsprojects.com/en/3.0.x/.

References

  1. Lodwick, G.S., Keats, T.E., Dorst, J.P.: The coding of roentgen images for computer analysis as applied to lung cancer. Radiology 81, 185–200 (1963)

    Article  Google Scholar 

  2. Zakirov, A., Kuleev, R., Timoshenko, A., Vladimirov, A.: Advanced approaches to computer-aided detection of thoracic diseases on chest X-rays. Appl. Math. Sci. 9, 4361–4369 (2015)

    Google Scholar 

  3. Qin, C., Yao, D., Shi, Y., Song, Z.: Computer-aided detection in chest radiography based on artificial intelligence: a survey. Biomed. Eng. Online 17, 113 (2018)

    Article  Google Scholar 

  4. Rajpurkar, P., et al.: Deep learning for chest radiograph diagnosis: a retrospective comparison of the CheXNeXt algorithm to practicing radiologists. PLoS Med. 15, e1002686 (2018)

    Article  Google Scholar 

  5. Brady, A.P.: Error and discrepancy in radiology: inevitable or avoidable? Insights Imaging 8, 171–182 (2017)

    Article  Google Scholar 

  6. Pati, S., et al.: Author correction: federated learning enables big data for rare cancer boundary detection. Nat. Commun. 14, 436 (2023)

    Article  Google Scholar 

  7. Quevedo, S., Domıngez, F., Pelaez, E.: Detection of pathologies in X-Ray chest images using a deep convolutional neural network with appropriate data augmentation techniques. In: 2022 IEEE ANDESCON, pp. 1–6 (2022)

    Google Scholar 

  8. Quevedo, S., Domıngez, F., Pelaez, E.: Detecting multi thoracic diseases in chest X-Ray images using deep learning techniques In: 2023 IEEE 13th International Conference on Pattern Recognition Systems (ICPRS), pp. 1–7 (2023)

    Google Scholar 

  9. Jaiswal, A.K., et al.: Identifying pneumonia in chest X-rays: a deep learning approach. Measurement 145, 511–518 (2019)

    Article  Google Scholar 

  10. Ebrahimighahnavieh, M.A., Luo, S., Chiong, R.: Deep learning to detect Alzheimer’s disease from neuroimaging: a systematic literature review. Comput. Methods Programs Biomed. 187, 105242 (2020)

    Article  Google Scholar 

  11. Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017)

    Google Scholar 

  12. Wang, X., et al.: ChestX-ray8: hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2097–2106 (2017)

    Google Scholar 

  13. Bustos, A., Pertusa, A., Salinas, J.-M., de la Iglesia-Vayá, M.: PadChest: a large chest X-ray image dataset with multi-label annotated reports. Med. Image Anal. 66, 101797 (2020)

    Google Scholar 

  14. Johnson, A.E., et al.: MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographs (2019). arXiv preprint arXiv:1901.07042

  15. Mandreoli, F., Ferrari, D., Guidetti, V., Motta, F., Missier, P.: Real-world data mining meets clinical practice: research challenges and perspective. Front. Big Data 99 (2022)

    Google Scholar 

  16. Quevedo, S., Merchán, F., Rivadeneira, R., Dominguez, F.X.: Evaluating apache OpenWhisk-FaaS. In: 2019 IEEE Fourth Ecuador Technical Chapters Meeting (ETCM), pp. 1–5 (2019)

    Google Scholar 

  17. Rajpurkar, P., et al.: CheXpedition: Investigating generalization challenges for translation of chest X-ray algorithms to the clinical setting (2020). arXiv preprint arXiv:2002.11379

  18. Cohen, J. P., Bertin, P., Frappier, V.: Chester: A web delivered locally computed chest X-ray disease prediction system (2019). arXiv preprint

    Google Scholar 

  19. Delgado, J., Clavijo, L., Soria, C., Ortega, J., Quevedo, S.: M-HEALTH system for detecting COVID-19 in chest X-Rays using deep learning and data security approaches. In: International Congress on Information and Communication Technology, pp. 73–86 (2023)

    Google Scholar 

  20. Pham, H.H., Nguyen, H.Q., Nguyen, H.T., Le, L.T., Khanh, L.: An accurate and explainable deep learning system improves interobserver agreement in the interpretation of chest radiograph. IEEE Access 10, 104512–104531 (2022)

    Article  Google Scholar 

  21. Yuan, Z., Yan, Y., Sonka, M., Yang, T.: Large-scale robust deep AUC maximization: a new surrogate loss and empirical studies on medical image classification. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3040–3049 (2021)

    Google Scholar 

  22. Selvaraju, R.R., et al.: Grad-CAM: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017)

    Google Scholar 

Download references

Acknowledgment

The Ecuadorian government has supported the first author under a SENESCYT scholarship.

Author information

Authors and Affiliations

  1. Electrical and Computer Engineering, Escuela Superior Politécnica del Litoral - ESPOL University, Guayaquil, Ecuador

    Sebastián Quevedo, Federico Domínguez & Enrique Peláez

  2. University of Antwerp, imec-IDLab, Antwerp, Belgium

    Hamed Behzadi-Khormouji

  3. Universidad Católica de Cuenca, XR-LAB, Cuenca, Ecuador

    Sebastián Quevedo

Authors
  1. Sebastián Quevedo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hamed Behzadi-Khormouji
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Federico Domínguez
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Enrique Peláez
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sebastián Quevedo .

Editor information

Editors and Affiliations

  1. ISEG, Universidade de Lisboa, Lisbon, Portugal

    Álvaro Rocha

  2. College of Engineering, The Ohio State University, Columbus, OH, USA

    Hojjat Adeli

  3. Institute of Data Science and Digital Technologies, Vilnius University, Vilnius, Lithuania

    Gintautas Dzemyda

  4. DCT, Universidade Portucalense, Porto, Portugal

    Fernando Moreira

  5. Institute of Information Technology, Lodz University of Technology, Łódz, Poland

    Aneta Poniszewska-Marańda

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Quevedo, S., Behzadi-Khormouji, H., Domínguez, F., Peláez, E. (2024). Deep Learning for Healthcare: A Web-Microservices System Ready for Chest Pathology Detection. In: Rocha, Á., Adeli, H., Dzemyda, G., Moreira, F., Poniszewska-Marańda, A. (eds) Good Practices and New Perspectives in Information Systems and Technologies. WorldCIST 2024. Lecture Notes in Networks and Systems, vol 986. Springer, Cham. https://doi.org/10.1007/978-3-031-60218-4_16

Download citation

Publish with us

Policies and ethics