Early Detection of Tuberculosis Using SVM and FCM

K. V. L. Keerthi, N. Nandhu Sri, O. Brunda Sree, Naguru Subbamma, O. Adithya Karthikeya, Abhinaya Varma N.

2025

Abstract

Tuberculosis (TB) continues to be a leading cause of morbidity and mortality worldwide and requires rapid and reliable diagnostic methods for early diagnosis. We present a thorough-based methodology for keeping a watch on TB at the early stage with the aid of Chest X-ray (CXR) images and Computer-Aided Diagnosis (CAD) through Machine Learning (ML) approaches. The proposed framework consists of several stages: input image acquisition, pre-processing, edge detection (Canny), fuzzy C-means segmentation, feature extraction, and, finally, support vector machine (SVM) classification of features. CXR images are first captured and processed to improve their quality and reduces noise. Then, edge detection techniques are used to enhance the prominent structures in the images. The next step involves applying Fuzzy C-means segmentation to accurately delineate the lung area, facilitating the extraction of potential TB lesions. Feature extraction is an essential phase in which features, that describe TB lesions, are extracted from the segmented regions. The features that have been used are a variety of statistical, textural, and morphological descriptors, providing rich data for the next step of classification. An SVM classifier is then trained on the extracted features to differentiate between TB-positive and TB-negative cases. In this work we proposed a framework which shows competitive results for early detection of TB from CXR images. It provides automated and improved diagnosis by incorporating ML algorithms, which might lower the workload of health professionals and enable early intervention for patients suffering from TB. A meta-analysis demonstrates the stable and reliable performance of the proposed method, highlighting its utility in the fight against TB, where it can supplement the work of expert radiologists in low-resource settings where such specialists may be scarce.

Paper Citation

in Harvard Style

Keerthi K., Sri N., Sree O., Subbamma N., Karthikeya O. and N. A. (2025). Early Detection of Tuberculosis Using SVM and FCM. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 512-518. DOI: 10.5220/0013915800004919

in Bibtex Style

@conference{icrdicct`2525,
author={K. Keerthi and N. Sri and O. Sree and Naguru Subbamma and O. Karthikeya and Abhinaya N.},
title={Early Detection of Tuberculosis Using SVM and FCM},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25},
year={2025},
pages={512-518},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013915800004919},
isbn={978-989-758-777-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25
TI - Early Detection of Tuberculosis Using SVM and FCM
SN - 978-989-758-777-1
AU - Keerthi K.
AU - Sri N.
AU - Sree O.
AU - Subbamma N.
AU - Karthikeya O.
AU - N. A.
PY - 2025
SP - 512
EP - 518
DO - 10.5220/0013915800004919
PB - SciTePress