Performance of support vector machines in pneumonia detection using chest x-ray images from Filipino cohorts

Authors

  • Melrose S. Tia Institute of Mathematical Sciences and Physics, University of the Philippines Los Baños
  • Lei Rigi P. Baltazar Institute of Mathematical Sciences and Physics, University of the Philippines Los Baños and Domingo AI Research Center
  • Ranzivelle Marianne Roxas-Villanueva Institute of Mathematical Sciences and Physics, University of the Philippines Los Baños
  • Beatrice J. Tiangco National Institutes of Health, University of the Philippine Manila and Department of Medicine, The Medical City
  • Ethel Dominique E. Viray Department of Medicine, The Medical City
  • Jason R. Albia Institute of Mathematical Sciences and Physics, University of the Philippines Los Baños and Domingo AI Research Center

Abstract

Studies have shown that computer-aided diagnosis (CAD) systems significantly improve the accuracy and speed of radiologic interpretations of chest x-ray images (CXR). In this study, we developed a machine learning-based CXR image classifier by optimizing support vector machine (SVM) to distinguish pneumonia from normal CXR images. The dataset used to develop the classifier model were generated through a retrospective clinical study conducted in the Philippines. We implemented feature extraction on the pre-processed CXR by considering four statistical feature sets: intensity histogram, invariant moments, Haralick features, and local binary pattern. Results show that the SVM classifier model has 88.33% accuracy, 86.57% precision, 90.63% sensitivity, 86.05% specificity, and 0.94 AUC in distinguishing pneumonia from normal CXR. These results illustrate the potential of a machine learning-based model to facilitate high sensitivity and automated screening of pneumonia.

SPP2021 Proceedings Cover

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Published

2021-10-14

Issue

2021: Proceedings of the 39th Samahang Pisika ng Pilipinas Physics Conference

Section

Biological and Medical Physics

License

By submitting their manuscript to the Samahang Pisika ng Pilipinas (SPP) for consideration, the Authors warrant that their work is original, does not infringe on existing copyrights, and is not under active consideration for publication elsewhere.
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All other associated economic and moral rights as granted by the Intellectual Property Code of the Philippines are maintained by the Authors.

How to Cite

[1]
“Performance of support vector machines in pneumonia detection using chest x-ray images from Filipino cohorts”, Proc. SPP, vol. 39, no. 1, pp. SPP–2021, Oct. 2021, Accessed: Mar. 30, 2026. [Online]. Available: https://proceedings.spp-online.org/article/view/SPP-2021-3C-06