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Pneumonia Detection Using Deep Learning Based Feature Extraction and Machine Learning

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Computer Vision and Image Processing (CVIP 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1777))

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Abstract

Pneumonia is a potentially fatal disease that accounts for huge loss of life worldwide, especially in paediatric cases. It can be caused by viral, bacterial, fungal or Covid-19 infection. In the case of Covid-19 Pneumonia, the disease progresses very swiftly if proper medical care is not provided for the patients. This work focuses on providing a model that can accurately detect Pneumonia from among various other pulmonary diseases. The proposed model uses CNN-based feature extractor along with machine learning classifiers: Random Forest (RF), Support Vector Machine (SVM) and Logistic Regression (LR). We have used five different datasets to overcome the concerns raised about generalization of the model in some of the previous works. Our proposed model gives encouraging results and shows marked improvement in classifying pneumonia.

B. H. Shekar and H. Hailu—Contributing authors.

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References

  1. Cruz, A.A.: Global surveillance, prevention and control of chronic respiratory diseases: a comprehensive approach. World Health Organization, Geneva (2007)

    Google Scholar 

  2. Wardlaw, T.M., Johansson, E.W., Hodge, M.J.: Pneumonia: the forgotten killer of children. UNICEF, New York (2006)

    Google Scholar 

  3. Gilani, Z., et al.: A literature review and survey of childhood pneumonia etiology studies: 2000–2010. Clin. Infect. Diseases 54(suppl_2), 102–108 (2012)

    Google Scholar 

  4. Wunderink, R.G., Waterer, G.: Advances in the causes and management of community acquired pneumonia in adults. BMJ 358, j2471 (2017)

    Google Scholar 

  5. Cunha, B.A., Brusch, J.L., et al.: Hospital-acquired pneumonia (nosocomial pneumonia) and ventilator-associated pneumonia. Drugs and Diseases (2018)

    Google Scholar 

  6. Aydogdu, M., Ozyilmaz, E., Aksoy, H., Gursel, G., Ekim, N.: Mortality prediction in community-acquired pneumonia requiring mechanical ventilation; values of pneumonia and intensive care unit severity scores. Tuberk. Toraks 58(1), 25–34 (2010)

    Google Scholar 

  7. Stephen, O., Sain, M., Maduh, U.J., Jeong, D.-U.: An efficient deep learning approach to pneumonia classification in healthcare. J. Healthcare Eng. 2019, 4180949 (2019)

    Google Scholar 

  8. Chowdhury, M.E., et al.: Wearable real-time heart attack detection and warning system to reduce road accidents. Sensors 19(12), 2780 (2019)

    Article  Google Scholar 

  9. Kallianos, K., et al.: How far have we come? artificial intelligence for chest radiograph interpretation. Clin. Radiol. 74(5), 338–345 (2019)

    Article  Google Scholar 

  10. Tahir, A.M., et al.: A systematic approach to the design and characterization of a smart insole for detecting vertical ground reaction force (vgrf) in gait analysis. Sensors 20(4), 957 (2020)

    Article  Google Scholar 

  11. Rajpurkar, P., et al.: Deep learning for chest radiograph diagnosis: A retrospective comparison of the chexnext algorithm to practicing radiologists. PLoS Med. 15(11), 1002686 (2018)

    Article  Google Scholar 

  12. Li, Q., bauthorCai, W., Wang, X., Zhou, Y., Feng, D.D., Chen, M.: Medical image classification with convolutional neural network. In: 2014 13th International Conference on Control Automation Robotics & Vision (ICARCV), pp. 844–848. IEEE (2014)

    Google Scholar 

  13. Chowdhury, M.E., et al.: Can AI help in screening viral and COVID-19 pneumonia? IEEE Access 8, 132665–132676 (2020)

    Article  Google Scholar 

  14. Bhandary, A., et al.: Deep-learning framework to detect lung abnormality-a study with chest x-ray and lung CT scan images. Pattern Recogn. Lett. 129, 271–278 (2020)

    Article  Google Scholar 

  15. Yadav, S.S., Jadhav, S.M.: Deep convolutional neural network based medical image classification for disease diagnosis. J. Big Data 6(1), 1–18 (2019). https://doi.org/10.1186/s40537-019-0276-2

    Article  Google Scholar 

  16. Kermany, D.S., et al.: Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 172(5), 1122–1131 (2018)

    Article  Google Scholar 

  17. Abiyev, R.H., Ismail, A.: Covid-19 and pneumonia diagnosis in x-ray images using convolutional neural networks. Mathematical Problems in Engineering 2021 (2021)

    Google Scholar 

  18. Xu, R., et al.: Unsupervised detection of pulmonary opacities for computer-aided diagnosis of covid-19 on CT images. In: 2020 25th International Conference on Pattern Recognition (ICPR), pp. 9007–9014. IEEE (2021)

    Google Scholar 

  19. Ibrahim, D.M., Elshennawy, N.M., Sarhan, A.M.: Deep-chest: Multi-classification deep learning model for diagnosing COVID-19, pneumonia, and lung cancer chest diseases. Comput. Biol. Med. 132, 104348 (2021)

    Article  Google Scholar 

  20. Gaur, L., Bhatia, U., Jhanjhi, N., Muhammad, G., Masud, M.: Medical image-based detection of covid-19 using deep convolution neural networks. Multimedia syst. 2021, 1–10 (2021)

    Google Scholar 

  21. Rahman, T., et al.: Transfer learning with deep convolutional neural network (CNN) for pneumonia detection using chest x-ray. Appl. Sci. 10(9), 3233 (2020)

    Article  Google Scholar 

  22. Elshennawy, N.M., Ibrahim, D.M.: Deep-pneumonia framework using deep learning models based on chest x-ray images. Diagnostics 10(9), 649 (2020)

    Article  Google Scholar 

  23. Yan, T., Wong, P.K., Ren, H., Wang, H., Wang, J., Li, Y.: Automatic distinction between COVID-19 and common pneumonia using multi-scale convolutional neural network on chest CT scans. Chaos Solitons Fract. 140, 110153 (2020)

    Article  MathSciNet  Google Scholar 

  24. Han, C.H., Kim, M., Kwak, J.T.: Semi-supervised learning for an improved diagnosis of COVID-19 in CT images. PLoS ONE 16(4), 0249450 (2021)

    Article  Google Scholar 

  25. Panwar, H., Gupta, P., Siddiqui, M.K., Morales-Menendez, R., Bhardwaj, P., Singh, V.: A deep learning and grad-cam based color visualization approach for fast detection of COVID-19 cases using chest x-ray and ct-scan images. Chaos Solitons Fract. 140, 110190 (2020)

    Article  MathSciNet  Google Scholar 

  26. Silva, P., et al.: COVID-19 detection in CT images with deep learning: a voting-based scheme and cross-datasets analysis. Inf. Med. Unlocked 20, 100427 (2020)

    Article  Google Scholar 

  27. Haghanifar, A., Majdabadi, M.M., Choi, Y., Deivalakshmi, S., Ko, S.: COVID-CXNet: Detecting COVID-19 in frontal chest x-ray images using deep learning. Multimedia Tools Appl. 81, 1–31 (2022)

    Google Scholar 

  28. Islam, M.Z., Islam, M.M., Asraf, A.: A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using x-ray images. Inf. Med. Unlocked 20, 100412 (2020)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science, Mangalore University, Mangalgangothri, Mangalore, 574199, Karnataka, India

    B. H. Shekar, Shazia Mannan & Habtu Hailu

Authors
  1. B. H. Shekar
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  2. Shazia Mannan
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  3. Habtu Hailu
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Corresponding author

Correspondence to Shazia Mannan .

Editor information

Editors and Affiliations

  1. Visvesvaraya National Institute of Technology Nagpur, Nagpur, India

    Deep Gupta

  2. Visvesvaraya National Institute of Technology Nagpur, Nagpur, India

    Kishor Bhurchandi

  3. Indian Institute of Technology Ropar, Rupnagar, India

    Subrahmanyam Murala

  4. Indian Institute of Technology Roorkee, Roorkee, India

    Balasubramanian Raman

  5. Indian Institute of Technology Roorkee, Roorkee, India

    Sanjeev Kumar

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Shekar, B.H., Mannan, S., Hailu, H. (2023). Pneumonia Detection Using Deep Learning Based Feature Extraction and Machine Learning. In: Gupta, D., Bhurchandi, K., Murala, S., Raman, B., Kumar, S. (eds) Computer Vision and Image Processing. CVIP 2022. Communications in Computer and Information Science, vol 1777. Springer, Cham. https://doi.org/10.1007/978-3-031-31417-9_45

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  • DOI: https://doi.org/10.1007/978-3-031-31417-9_45

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-31416-2

  • Online ISBN: 978-3-031-31417-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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