Skip to main content
SpringerLink
Log in

Multitask Deep Convolutional Neural Network with Attention for Pulmonary Tuberculosis Detection and Weak Localization of Pathological Manifestations in Chest X-Ray

  • Conference paper
  • First Online:
Pan-African Conference on Artificial Intelligence (PanAfriConAI 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2068))

Included in the following conference series:

  • 25 Accesses

Abstract

Pulmonary tuberculosis (PTB) is a highly fatal bacterial infection that affects the lungs. Chest radiography is a commonly used technique for PTB diagnosis. Interpreting chest X-ray images for features like cavitation, consolidation, and nodules poses challenges due to low contrast between lesions and surrounding tissue, and the complexity of identifying features for intricate disorders. To address these challenges, researchers have proposed using deep learning techniques to detect and mark areas of TB infection in chest X-rays. However, fully supervised semantic segmentation requires massive large pixel-by-pixel labeled images, which is time-consuming, expensive, and subjective. As a result, there is growing interest in weak localization techniques, a method identifying disease pathologies without pixel-level labeling. Hence, this study focuses on developing a deep learning model for weakly supervised segmentation and localization of radiographic manifestations of PTB from chest X-rays (CXR), using commonly used public datasets for TB identification. We proposed multi-scale attention using the DenseNet-121 model as a backbone network. First, a class activation map is calculated at different levels of the backbone network using the last feature map and the global average pooling at each specific level. Finally, the class activation map is combined using a convex combination and passed to the sigmoid functions. This approach is powerful for classifying and localizing disease pathology in CXR. We achieved a localization accuracy of 83% for T (IoU) = 0.1 and the classification AUC, accuracy and \(F_1\) score are 98%, 98%, and 97% respectively. This result indicates the model has a promising performance in both the classification and localization of PTB manifestations.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 59.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 79.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Al Ubaidi, B.: The radiological diagnosis of pulmonary tuberculosis (tb) in primary care. RadioPaedia 4, 73 (2018)

    Google Scholar 

  2. Ayano, Y.M., Schwenker, F., Dufera, B.D., Debelee, T.G.: Interpretable machine learning techniques in ECG-based heart disease classification: a systematic review. Diagnostics 13(1), 111 (2022). https://doi.org/10.3390/diagnostics13010111

    Article  Google Scholar 

  3. Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009)

    Google Scholar 

  4. Bonyani, M., Yeganli, F., Yeganli, S.F.: Fast and interpretable deep learning pipeline for breast cancer recognition. In: 2022 Medical Technologies Congress (TIPTEKNO), pp. 1–4 (2022)

    Google Scholar 

  5. Burrill, J., Williams, C.J., Bain, G., Conder, G., Hine, A.L., Misra, R.R.: Tuberculosis: a radiologic review. Radiographics 27(5), 1255–1273 (2007)

    Article  Google Scholar 

  6. Caws, M., Marais, B., Heemskerk, D., Farrar, J.: Tuberculosis in adults and children (2015)

    Google Scholar 

  7. Chakaya, J., et al.: Global tuberculosis report 2020-reflections on the global tb burden, treatment and prevention efforts. Int. J. Infect. Dis. 113, S7–S12 (2021)

    Article  Google Scholar 

  8. Chakaya, J., et al.: The who global tuberculosis 2021 report–not so good news and turning the tide back to end tb. Int. J. Infect. Dis. (2022)

    Google Scholar 

  9. Dasanayaka, S., Shantha, V., Silva, S., Meedeniya, D., Ambegoda, T.: Interpretable machine learning for brain tumour analysis using MRI and whole slide images. Softw. Impacts 13, 100340 (2022). https://doi.org/10.1016/j.simpa.2022.100340

    Article  Google Scholar 

  10. Ding, F., et al.: Hierarchical attention networks for medical image segmentation. arXiv preprint arXiv:1911.08777 (2019)

  11. Niknejad, M., Gaillard, F.: Tuberculosis (pulmonary manifestations). J. Fam. Med. Dis. Prev. (2022). https://doi.org/10.53347/rID-8631

  12. Hoog, A., et al.: A systematic review of the sensitivity and specificity of symptom and chest radiography screening for active pulmonary tuberculosis in hiv-negative persons and persons with unknown hiv status (2013). https://doi.org/10.13140/RG.2.2.19848.06406

  13. van’t Hoog, A.H., et al.: Screening strategies for tuberculosis prevalence surveys: the value of chest radiography and symptoms. PloS One 7(7), e38691 (2012)

    Google Scholar 

  14. 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 

  15. Ilse, M., Tomczak, J., Welling, M.: Attention-based deep multiple instance learning. In: International Conference on Machine Learning, pp. 2127–2136. PMLR (2018)

    Google Scholar 

  16. Kim, I., Rajaraman, S., Antani, S.: Visual interpretation of convolutional neural network predictions in classifying medical image modalities. Diagnostics 9(2), 38 (2019)

    Article  Google Scholar 

  17. Kolesnikov, A., Lampert, C.H.: Seed, expand and constrain: three principles for weakly-supervised image segmentation. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 695–711. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46493-0_42

    Chapter  Google Scholar 

  18. Liu, J., Zhao, G., Fei, Y., Zhang, M., Wang, Y., Yu, Y.: Align, attend and locate: chest x-ray diagnosis via contrast induced attention network with limited supervision. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10632–10641 (2019)

    Google Scholar 

  19. Liu, Y., Wu, Y.H., Ban, Y., Wang, H., Cheng, M.M.: Rethinking computer-aided tuberculosis diagnosis. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2646–2655 (2020)

    Google Scholar 

  20. Liu, Y., Wu, Y.H., Zhang, S.C., Liu, L., Wu, M., Cheng, M.M.: Revisiting computer-aided tuberculosis diagnosis. arXiv preprint arXiv:2307.02848 (2023)

  21. Ouyang, X., et al.: Learning hierarchical attention for weakly-supervised chest x-ray abnormality localization and diagnosis. IEEE Trans. Med. Imaging 40(10), 2698–2710 (2020)

    Article  Google Scholar 

  22. Pan, C., et al.: Computer-aided tuberculosis diagnosis with attribute reasoning assistance. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI 2022. LNCS, vol. 13431, pp. 623–633. Springer, Heidelberg (2022). https://doi.org/10.1007/978-3-031-16431-6_59

    Chapter  Google Scholar 

  23. Qi, B., et al.: Gren: graph-regularized embedding network for weakly-supervised disease localization in x-ray images. arXiv preprint arXiv:2107.06442 (2021)

  24. Rajaraman, S., Folio, L.R., Dimperio, J., Alderson, P.O., Antani, S.K.: Improved semantic segmentation of tuberculosis-consistent findings in chest x-rays using augmented training of modality-specific u-net models with weak localizations. Diagnostics 11(4), 616 (2021)

    Article  Google Scholar 

  25. Rajaraman, S., Guo, P., Xue, Z., Antani, S.K.: A deep modality-specific ensemble for improving pneumonia detection in chest x-rays. Diagnostics 12(6), 1442 (2022)

    Article  Google Scholar 

  26. Ryu, Y.J.: Diagnosis of pulmonary tuberculosis: recent advances and diagnostic algorithms. Tubercul. Respirat. Dis. 78(2), 64–71 (2015)

    Article  Google Scholar 

  27. Sedai, S., Mahapatra, D., Ge, Z., Chakravorty, R., Garnavi, R.: Deep multiscale convolutional feature learning for weakly supervised localization of chest pathologies in x-ray images. In: International Workshop on Machine Learning in Medical Imaging, pp. 267–275 (2018)

    Google Scholar 

  28. Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: 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 

  29. Simonyan, K., Vedaldi, A., Zisserman, A.: Deep inside convolutional networks: visualising image classification models and saliency maps. arXiv preprint arXiv:1312.6034 (2013)

  30. Singh, A., et al.: Deep learning for automated screening of tuberculosis from Indian chest x-rays: analysis and update. arXiv preprint arXiv:2011.09778 (2020)

  31. Steingart, K.R., et al.: Xpert® mtb/rif assay for pulmonary tuberculosis and rifampicin resistance in adults. Cochrane Database System. Rev. (2013)

    Google Scholar 

  32. Tang, Y., Wang, X., Harrison, A.P., Lu, L., Xiao, J., Summers, R.M.: Attention-guided curriculum learning for weakly supervised classification and localization of thoracic diseases on chest radiographs. In: Shi, Y., Suk, H.-I., Liu, M. (eds.) MLMI 2018. LNCS, vol. 11046, pp. 249–258. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00919-9_29

    Chapter  Google Scholar 

  33. Vezhnevets, A., Buhmann, J.M.: Towards weakly supervised semantic segmentation by means of multiple instance and multitask learning. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3249–3256. IEEE (2010)

    Google Scholar 

  34. Vonasek, B., et al.: Screening tests for active pulmonary tuberculosis in children. Cochrane Database System. Rev. (2021)

    Google Scholar 

  35. Wang, X., Peng, Y., Lu, L., Lu, Z., Summers, R.M.: Tienet: text-image embedding network for common thorax disease classification and reporting in chest x-rays. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9049–9058 (2018)

    Google Scholar 

  36. Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: European Conference on Computer Vision, pp. 818–833 (2014)

    Google Scholar 

  37. Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2921–2929 (2016)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Ethiopian Artificial Intelligence Institute, 40782, Addis Ababa, Ethiopia

    Degaga Wolde Feyisa, Yehualashet Megersa Ayano & Taye Girma Debelee

  2. Department of Computer Engineering, Addis Ababa Science and Technology University, 120611, Addis Ababa, Ethiopia

    Taye Girma Debelee

  3. Department of Radiology, School of Medicine, Addis Ababa University, 11760, Addis Ababa, Ethiopia

    Samuel Sisay Hailu

Authors
  1. Degaga Wolde Feyisa
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yehualashet Megersa Ayano
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Taye Girma Debelee
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Samuel Sisay Hailu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Degaga Wolde Feyisa .

Editor information

Editors and Affiliations

  1. Ethiopian Artificial Intelligence Instit, Addis Adaba, Ethiopia

    Taye Girma Debelee

  2. HAWK University of Applied Sciences and Arts, Göttingen, Germany

    Achim Ibenthal

  3. Universität Ulm, Ulm, Germany

    Friedhelm Schwenker

  4. Ethiopian Artificial Intelligence Instit, Addis Ababa, Ethiopia

    Yehualashet Megersa Ayano

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

Wolde Feyisa, D., Megersa Ayano, Y., Girma Debelee, T., Sisay Hailu, S. (2024). Multitask Deep Convolutional Neural Network with Attention for Pulmonary Tuberculosis Detection and Weak Localization of Pathological Manifestations in Chest X-Ray. In: Debelee, T.G., Ibenthal, A., Schwenker, F., Megersa Ayano, Y. (eds) Pan-African Conference on Artificial Intelligence. PanAfriConAI 2023. Communications in Computer and Information Science, vol 2068. Springer, Cham. https://doi.org/10.1007/978-3-031-57624-9_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-57624-9_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-57623-2

  • Online ISBN: 978-3-031-57624-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation