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Computer-Aided Diagnosis of Pneumothorax Through X-Ray Images Using Deep Learning—A Review

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Prognostic Models in Healthcare: AI and Statistical Approaches

Part of the book series: Studies in Big Data ((SBD,volume 109))

Abstract

A pneumothorax is a severe ailment that can result in mortality in humans due to shortness of breath. Due to its complicated features and poor contrast of the disease areas, pneumothorax is a life-threatening but simple thoracic ailment that is hard to detect using chest X-ray imaging. The automated diagnosis of pneumothorax in health surveillance is difficult for radiologists. Early detection of a pneumothorax is crucial for improving treatment outcomes and patient survival. In the medical field, the identification of pneumothorax through image processing is a tricky task. Recently, a rise of interest has been noticed in employing deep learning algorithms to aid pneumothorax detection. Nowadays, different medical imaging tools are available to detect specific diseases. Chest radiographs are widely used to diagnose pneumothorax. Detection of pneumothorax at early stages can overcome the treatment difficulties. This chapter evaluates several innovative technologies and research that could help detect pneumothorax automatically. Artificial intelligence (AI) provides a significant result for automated pneumothorax (PTX) detection. Research has been done to see pneumothorax disease automatically through the chest radiograph. This article abstracts previous articles for detecting PTX from CXRs through machine and deep learning and also discusses different publicly available datasets. This study provides a detailed overview and discusses the existing literature’s goodness and limitation. This literature chapter helps the researchers to find an optimal way to solve this problem and gives direction on which technique provides a better result.

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References

  1. Pneumothorax Detection: AI for Health. https://icts.com.sg/pneumothorax/. Accessed 31 Oct 2021

  2. Zarogoulidis, P., et al.: Pneumothorax: from definition to diagnosis and treatment. J. Thorac. Dis. 6(Suppl 4), S372–S376 (2014). https://doi.org/10.3978/j.issn.2072-1439.2014.09.24

    Article  Google Scholar 

  3. Pneumothorax (Collapsed Lung): Symptoms, Causes, and More. Healthline. 12 Oct 2021. https://www.healthline.com/health/collapsed-lung. Accessed 31 Oct 2021

  4. Collapsed Lung (Pneumothorax): Symptoms, Causes and Treatment. Cleveland Clinic. https://my.clevelandclinic.org/health/diseases/15304-collapsed-lung-pneumothorax. Accessed 03 Jan 2022

  5. Subotic-Kerry, M., King, C., O’Moore, K., Achilles, M., O’Dea, B.: General practitioners’ attitudes toward a web-based mental health service for adolescents: implications for service design and delivery. JMIR Hum. Factors 5(1), e12 (2018). https://doi.org/10.2196/humanfactors.8913

    Article  Google Scholar 

  6. CT Scan (Computed Tomography): What is It, Preparation and Test Details. Cleveland Clinic. https://my.clevelandclinic.org/health/diagnostics/4808-ct-computed-tomography-scan. Accessed 03 Jan 2022

  7. Pneumothorax CT Scan. https://www.ncbi.nlm.nih.gov/books/NBK441885/figure/article-27370.image.f3/?report=objectonly. Accessed 31 Oct 2021

  8. Pneumothorax (Collapsed Lung): Symptoms, Causes, and More. Healthline. https://www.ejinme.com/article/S0953-6205(20)30455-6/fulltext. Accessed 31 Oct 2021

  9. Pneumothorax Chest X-ray scan. https://www.ncbi.nlm.nih.gov/books/NBK538316/figure/article-27372.image.f1/. Accessed 31 Oct 2021

  10. Pham, H.H., Le, T.T., Tran, D.Q., Ngo, D.T., Nguyen, H.Q.: Interpreting chest X-rays via CNNs that exploit hierarchical disease dependencies and uncertainty labels. Neurocomputing 437, 186–194 (2021). https://doi.org/10.1016/j.neucom.2020.03.127

    Article  Google Scholar 

  11. Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., Summers, R.M.: ChestX-ray8: hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3462–3471. CVPR, 2017. https://doi.org/10.1109/CVPR.2017.369

  12. NIH Chest X-ray Dataset for 14 Common Thorax Disease Categories. https://academictorrents.com/details/557481faacd824c83fbf57dcf7b6da9383b3235a. Accessed 31 Oct 2021

  13. Filice, R.W., et al.: Crowdsourcing pneumothorax annotations using machine learning annotations on the NIH chest X-ray dataset. J. Digit. Imaging 33(2), 490–496 (2020). https://doi.org/10.1007/s10278-019-00299-9

    Article  Google Scholar 

  14. SIM-ARC Chest X-ray Dataset. https://www.kaggle.com/c/siim-acr-pneumothorax-segmentation. Accessed 31 Oct 2021

  15. CheXpert chest X-ray dataset sample. https://aimi.stanford.edu/chexpert-chest-x-rays. Accessed 31 Oct 2021

  16. CheXpert: Chest X-Rays. Center for Artificial Intelligence in Medicine and Imaging. https://aimi.stanford.edu/chexpert-chest-x-rays. Accessed 31 Oct 2021

  17. Johnson, A.E.W., et al.: MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographs. ArXiv 190107042 Cs Eess, Nov 2019. Accessed: 31 Oct 2021 [Online]. Available: http://arxiv.org/abs/1901.07042

  18. Random Sample of NIH Chest X-Ray Dataset. https://kaggle.com/nih-chest-xrays/sample. Accessed 31 Oct 2021

  19. Sze-To, A., Riasatian, A., Tizhoosh, H.R.: Searching for pneumothorax in X-ray images using autoencoded deep features. Sci. Rep. 11(1), 9817 (2021). https://doi.org/10.1038/s41598-021-89194-4

    Article  Google Scholar 

  20. Li, X., et al.: Deep learning-enabled system for rapid pneumothorax screening on chest CT. Eur. J. Radiol. 120, 108692 (2019). https://doi.org/10.1016/j.ejrad.2019.108692

    Article  Google Scholar 

  21. Wang, H., Gu, H., Qin, P., Wang, J.: CheXLocNet: automatic localization of pneumothorax in chest radiographs using deep convolutional neural networks. PLoS ONE 15(11), e0242013 (2020). https://doi.org/10.1371/journal.pone.0242013

    Article  Google Scholar 

  22. Chan, Y.-H., Zeng, Y.-Z., Wu, H.-C., Wu, M.-C., Sun, H.-M.: Effective pneumothorax detection for chest X-ray images using local binary pattern and support vector machine. J. Healthc. Eng. 2018, e2908517 (2018). https://doi.org/10.1155/2018/2908517

    Article  Google Scholar 

  23. Lindsey, T., Lee, R., Grisell, R., Vega, S., Veazey, S.: Automated pneumothorax diagnosis using deep neural networks. In: Iberoamerican congress on pattern recognition (pp. 723–731). Springer, Cham

    Google Scholar 

  24. Taylor, A.G., Mielke, C., Mongan, J.: Automated detection of moderate and large pneumothorax on frontal chest X-rays using deep convolutional neural networks: a retrospective study. PLoS Med. 15(11), e1002697 (2018). https://doi.org/10.1371/journal.pmed.1002697

    Article  Google Scholar 

  25. Park, S., et al.: Application of deep learning-based computer-aided detection system: detecting pneumothorax on chest radiograph after biopsy. Eur. Radiol. 29(10), 5341–5348 (2019). https://doi.org/10.1007/s00330-019-06130-x

    Article  Google Scholar 

  26. Röhrich, S., Schlegl, T., Bardach, C., Prosch, H., Langs, G.: Deep learning detection and quantification of pneumothorax in heterogeneous routine chest computed tomography. Eur. Radiol. Exp. 4, 26 (2020). https://doi.org/10.1186/s41747-020-00152-7

    Article  Google Scholar 

  27. Geva, O., Zimmerman-Moreno, G., Lieberman, S., Konen, E., Greenspan, H.: Pneumothorax detection in chest radiographs using local and global texture signatures. In: Medical Imaging 2015: Computer-Aided Diagnosis, vol. 9414, pp. 448–454 (2015). https://doi.org/10.1117/12.2083128

  28. Rajpurkar, P., et al.: Deep learning for chest radiograph diagnosis: a retrospective comparison of the CheXNeXt algorithm to practicing radiologists. PLOS Med. 15(11), e1002686 (2018). https://doi.org/10.1371/journal.pmed.1002686

    Article  Google Scholar 

  29. Rajpurkar, P., et al.: CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning. ArXiv 171105225 Cs Stat, Dec 2017. Accessed: 02 Oct 2021 [Online]. Available: http://arxiv.org/abs/1711.05225

  30. Cho, Y., Kim, J.S., Lim, T.H., Lee, I., Choi, J.: Detection of the location of pneumothorax in chest X-rays using small artificial neural networks and a simple training process. Sci. Rep. 11(1), 13054 (2021). https://doi.org/10.1038/s41598-021-92523-2

    Article  Google Scholar 

  31. Xu, S., Guo, J., Zhang, G., Bie, R.: Automated detection of multiple lesions on chest X-ray images: classification using a neural network technique with association-specific contexts. Appl. Sci. 10(5) (2020). Art. no. 5. https://doi.org/10.3390/app10051742

  32. Abiyev, R.H., Ma’aitah, M.K.S.: Deep convolutional neural networks for chest diseases detection. J. Healthc. Eng. 2018, e4168538 (2018). https://doi.org/10.1155/2018/4168538

  33. Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., Summers, R.: ChestX-Ray8: hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition. CVPR, 2017. https://doi.org/10.1109/CVPR.2017.369

  34. Gooßen, A., et al.: Pneumothorax Detection and Localization in Chest Radiographs: A Comparison of Deep Learning Approaches (2019)

    Google Scholar 

  35. Jakhar, K., Kaur, A., Gupta, D.M.: Pneumothorax Segmentation: Deep Learning Image Segmentation to predict Pneumothorax. ArXiv 191207329 Cs, Apr 2021. Accessed: 02 Oct 2021 [Online]. Available: http://arxiv.org/abs/1912.07329

  36. Wang, Q., et al.: Automated segmentation and diagnosis of pneumothorax on chest X-rays with fully convolutional multi-scale ScSE-DenseNet: a retrospective study. BMC Med. Inform. Decis. Mak. 20(14), 317 (2020). https://doi.org/10.1186/s12911-020-01325-5

    Article  Google Scholar 

  37. Sogancioglu, E., Çallı, E., van Ginneken, B., van Leeuwen, K.G., Murphy, K.: Deep learning for chest X-ray analysis: a survey. Med. Image Anal. 72, 102125 (2021). https://doi.org/10.1016/j.media.2021.102125

    Article  Google Scholar 

  38. Jun, T.J., Kim, D., Kim, D.: Automated Diagnosis of Pneumothorax Using an Ensemble of Convolutional Neural Networks with Multi-Sized Chest Radiography Images, p. 18

    Google Scholar 

  39. Pan, I., Agarwal, S., Merck, D.: Generalizable inter-institutional classification of abnormal chest radiographs using efficient convolutional neural networks. J. Digit. Imaging 32(5), 888–896 (2019). https://doi.org/10.1007/s10278-019-00180-9

    Article  Google Scholar 

  40. Blumenfeld, A., Konen, E., Greenspan, H.: Pneumothorax detection in chest radiographs using convolutional neural networks. In: Medical Imaging 2018: Computer-Aided Diagnosis, vol. 10575, p. 1057504 (2018). Proc. SPIE. Accessed 27 February 2018. https://doi.org/10.1117/12.2292540

  41. Crosby, J., Rhines, T., Li, F., MacMahon, H., Giger, M.: Deep learning for pneumothorax detection and localization using networks fine-tuned with multiple institutional datasets. In: Medical Imaging 2020: Computer-Aided Diagnosis, vol. 11314, pp. 70–74 (2020). https://doi.org/10.1117/12.2549709

  42. Wang, X., et al.: Automatic segmentation of pneumothorax in chest radiographs based on a two-stage deep learning method. IEEE Trans. Cogn. Dev. Syst., 14, 1 (2020). https://doi.org/10.1109/TCDS.2020.3035572

  43. Yimer, F., Tessema, A.W., Simegn, G.L.: Multiple Lung Diseases Classification from Chest X-Ray Images using Deep Learning Approach. https://www.researchgate.net/publication/355203166_Multiple_Lung_Diseases_Classification_from_Chest_X-Ray_Images_using_Deep_Learning_approach. Accessed 22 Dec 2021

  44. Wang, Z., Haddad, R.J.: Automated diagnosis of pneumothorax X-ray images utilizing deep convolutional neural network. In: 2020 SoutheastCon, pp. 1–2 (2020). https://doi.org/10.1109/SoutheastCon44009.2020.9249683

  45. Groza, V., Kuzin, A.: Pneumothorax segmentation with effective conditioned post-processing in chest X-ray. In: IEEE Conference Publication. IEEE Xplore. https://ieeexplore.ieee.org/document/9153444. Accessed 22 Dec 2021

  46. Neri, E., et al.: What the radiologist should know about artificial intelligence—an ESR white paper. Insights Imaging 10(1), 44 (2019). https://doi.org/10.1186/s13244-019-0738-2

    Article  Google Scholar 

  47. Montagnon, E., et al.: Deep learning workflow in radiology: a primer. Insights Imaging 11(1), 22 (2020). https://doi.org/10.1186/s13244-019-0832-5

    Article  Google Scholar 

  48. Chan, H.-P., Samala, R.K., Hadjiiski, L.M., Zhou, C.: Deep learning in medical image analysis. Adv. Exp. Med. Biol. 1213, 3–21 (2020). https://doi.org/10.1007/978-3-030-33128-3_1

    Article  Google Scholar 

  49. Viniavskyi, O., Dobko, M., Dobosevych, O.: Weakly-Supervised Segmentation for Disease Localization in Chest X-Ray Images. ArXiv 200700748 Cs, July 2020. Accessed: 27 Dec 2021 [Online]. Available: http://arxiv.org/abs/2007.00748

  50. Yao, L., Prosky, J. Poblenz, E., Covington, B., Lyman, K.: Weakly Supervised Medical Diagnosis and Localization from Multiple Resolutions. ArXiv 180307703 Cs, Mar 2018. Accessed: 27 Dec 2021 [Online]. Available: http://arxiv.org/abs/1803.07703

  51. Salehinejad, H., Valaee, S., Dowdell, T., Colak, E., Barfett, J.: Generalization of Deep Neural Networks for Chest Pathology Classification in X-Rays Using Generative Adversarial Networks. ArXiv 171201636 Cs, Feb 2018. Accessed: 27 Dec 2021 [Online]. Available: http://arxiv.org/abs/1712.01636

  52. Shoukat, A., Akbar, S.: Artificial Intelligence Techniques for Glaucoma Detection Through Retinal Images. Artificial Intelligence and Internet of Things: Applications in Smart Healthcare, p. 209 (2021)

    Google Scholar 

  53. Gull, S., Akbar, S.: Artificial intelligence in brain tumor detection through MRI scans. Artif. Intell. Internet Things, 241–276 (2021)

    Google Scholar 

  54. Hassan, S.A., et al.: Artificial intelligence in coronavirus detection: recent findings and future perspectives. In: Intelligent Computing Applications for COVID-19, pp. 23–48 (2021)

    Google Scholar 

  55. Akbar, S., et al.: Arteriovenous ratio and papilledema based hybrid decision support system for detection and grading of hypertensive retinopathy. Comput. Methods Programs Biomed. 154, 123–141 (2018)

    Article  Google Scholar 

  56. Akbar, S., et al.: Decision support system for detection of hypertensive retinopathy using arteriovenous ratio. Artif. Intell. Med. 90, 15–24 (2018)

    Article  Google Scholar 

  57. Akbar, S., et al.: Decision support system for detection of papilledema through fundus retinal images. J. Med. Syst. 41(4), 66 (2017)

    Google Scholar 

  58. Saba, T., et al.: Automatic detection of papilledema through fundus retinal images using deep learning. Microsc. Res. Tech. 84(12), 3066–3077 (2021)

    Google Scholar 

  59. Gull, S., Akbar, S., Khan, H.U.: Automated detection of brain tumor through magnetic resonance images using convolutional neural network. BioMed Res. Int. (2021)

    Google Scholar 

  60. Gull, S., Akbar, S., Safdar, K.: An interactive deep learning approach for brain tumor detection through 3D-magnetic resonance images. In: Accepted in 18th International Conference on Frontiers of Information Technology (FIT), Islamabad, Pakistan (2021)

    Google Scholar 

  61. Gull, S., Akbar, S., Shoukat, I.A.: A deep transfer learning approach for automated detection of brain tumor through magnetic resonance imaging. In: 2021 International Conference on Innovative Computing (ICIC), pp. 1–6 (2021). https://doi.org/10.1109/ICIC53490.2021.9692967

  62. Shoukat, A., Akbar, S., Hassan, S.A.E., Rehman, A., Ayesha, N.: Glaucoma diagnosis through convolutional neural network using fundus image. In: Accepted in 18th International Conference on Frontiers of Information Technology (FIT), Islamabad, Pakistan (2021)

    Google Scholar 

  63. Shoukat, A., Akbar, S., Safdar, K.: A deep learning-based automatic method for early detection of the glaucoma using fundus images. In: 2021 International Conference on Innovative Computing (ICIC), pp. 1–6 (2021). https://doi.org/10.1109/ICIC53490.2021.9693078

  64. . Akbar, S., Hassan, S.A.E., Shoukat, A., Alyami, J., Bahaj, S.A.: Detection of microscopic glaucoma through fundus images using deep transfer learning approach. In: Accepted in Microscopy Research and Technique, 27 Jan 2022

    Google Scholar 

  65. Safdar, K., Akbar, S., Gull, S.: An automated deep learning based ensemble approach for malignant melanoma detection using dermoscopy images. In: Accepted in 18th International Conference on Frontiers of Information Technology (FIT), Islamabad, Pakistan (2021)

    Google Scholar 

  66. Safdar, K., Akbar, S., Shoukat, A.: A majority voting based ensemble approach of deep learning classifiers for automated melanoma detection. In: 2021 International Conference on Innovative Computing (ICIC), pp. 1–6 (2021). https://doi.org/10.1109/ICIC53490.2021.9692915

  67. Ahmad, M.F., Akbar, S., Hassan, S.A.E., Rehman, A., Ayesha, N.: Deep learning approach to diagnose Alzheimer’s disease through magnetic resonance images. In: 2021 International Conference on Innovative Computing (ICIC), pp. 1–6 (2021). https://doi.org/10.1109/ICIC53490.2021.9693041

  68. Hassan, S.A.E., et al.: Deep learning-based automatic detection of central serous retinopathy using optical coherence tomographic images. In: 2021 1st International Conference on Artificial Intelligence and Data Analytics (CAIDA). IEEE (2021)

    Google Scholar 

  69. Hassan, S.A., et al.: Recent developments in detection of central serous retinopathy through imaging and artificial intelligence techniques—a review. IEEE Access (2021)

    Google Scholar 

  70. Akram, M.U., et al.: Data on fundus images for vessels segmentation, detection of hypertensive retinopathy, diabetic retinopathy and papilledema. Data Brief 29, 105282 (2020)

    Article  Google Scholar 

  71. Akbar, S., et al.: AVRDB: annotated dataset for vessel segmentation and calculation of arteriovenous ratio. In: Proceedings of the 21th International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV’17) (2017)

    Google Scholar 

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Acknowledgements

This study was supported by Riphah Artificial Intelligence Research (RAIR) Lab, Riphah International University, Faisalabad, Pakistan.

Conflict of Interest

The authors declare that they have no conflict of interest.

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Authors and Affiliations

  1. Riphah College of Computing, Riphah International University, Faisalabad Campus, Faisalabad, Pakistan

    Farwa Urooj, Shahzad Akbar, Syed Ale Hassan & Saba Firdous

  2. School of Electrical Engineering and Computer Science, National University of Science and Technology (NUST), Islamabad, Pakistan

    Muhammad Jawad Bashir

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  1. Farwa Urooj
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  2. Shahzad Akbar
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  4. Saba Firdous
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  5. Muhammad Jawad Bashir
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Correspondence to Shahzad Akbar .

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Editors and Affiliations

  1. Artificial Intelligence and Data Analytics Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia

    Tanzila Saba

  2. Artificial Intelligence and Data Analytics Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia

    Amjad Rehman

  3. Artificial Intelligence and Data Science, Jio Institute, Ulwe, Navi Mumbai, India

    Sudipta Roy

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Urooj, F., Akbar, S., Hassan, S.A., Firdous, S., Bashir, M.J. (2022). Computer-Aided Diagnosis of Pneumothorax Through X-Ray Images Using Deep Learning—A Review. In: Saba, T., Rehman, A., Roy, S. (eds) Prognostic Models in Healthcare: AI and Statistical Approaches. Studies in Big Data, vol 109. Springer, Singapore. https://doi.org/10.1007/978-981-19-2057-8_15

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