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Pneumonia Detection on Chest X-Ray Using Machine Learning Paradigm

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Proceedings of 3rd International Conference on Computer Vision and Image Processing

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1022))

Abstract

The chest radiograph is the globally accepted standard used for analysis of pulmonary diseases. This paper presents a method for automatic detection of pneumonia on segmented lungs using machine learning paradigm. The paper focuses on pixels in lungs segmented ROI (Region of Interest) that are more contributing toward pneumonia detection than the surrounding regions, thus the features of lungs segmented ROI confined area is extracted. The proposed method has been examined using five benchmarked classifiers named Multilayer Perceptron, Random forest, Sequential Minimal Optimization (SMO), Logistic Regression, and Classification via Regression. A dataset of a total of 412 chest X-ray images containing 206 normal and 206 pneumonic cases from the ChestX-ray14 dataset are used in experiments. The performance of the proposed method is compared with the traditional method using benchmarked classifiers. Experimental results demonstrate that the proposed method outperformed the existing method attaining a significantly higher accuracy of 95.63% with the Logistic Regression classifier and 95.39% with Multilayer Perceptron.

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Acknowledgements

The authors would like to thank Dr. Javahar Agrawal, Diabetologist and Senior Consulting Physician, Lifeworth Super Speciality Hospital, Raipur and Dr. A. D. Raje, Consulting Radiologist, MRI Diagnostic Institute, Choubey Colony, Raipur for their valuable guidance.

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Authors and Affiliations

  1. Department of Computer Applications, National Institute of Technology, Raipur, India

    Tej Bahadur Chandra & Kesari Verma

Authors
  1. Tej Bahadur Chandra
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  2. Kesari Verma
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Corresponding author

Correspondence to Tej Bahadur Chandra .

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Editors and Affiliations

  1. Computer Vision and Pattern Recognition Unit, Indian Statistical Institute, Kolkata, India

    Bidyut B. Chaudhuri

  2. Department of Advanced Information Technology and Computer Sciences, Tokyo Institute of Agriculture and Technology, Koganei, Tokyo, Japan

    Masaki Nakagawa

  3. Department of Computer Science, Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, Jabalpur, Madhya Pradesh, India

    Pritee Khanna

  4. Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Sanjeev Kumar

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Chandra, T.B., Verma, K. (2020). Pneumonia Detection on Chest X-Ray Using Machine Learning Paradigm. In: Chaudhuri, B., Nakagawa, M., Khanna, P., Kumar, S. (eds) Proceedings of 3rd International Conference on Computer Vision and Image Processing. Advances in Intelligent Systems and Computing, vol 1022. Springer, Singapore. https://doi.org/10.1007/978-981-32-9088-4_3

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  • DOI: https://doi.org/10.1007/978-981-32-9088-4_3

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-32-9087-7

  • Online ISBN: 978-981-32-9088-4

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