@article {10.3844/jcssp.2025.413.423,
article_type = {journal},
title = {Classification of X-Ray Images Using Convolutional Neural Network and Automatic Hyper-Parameter Selection to Detect Tuberculosis (TB)},
author = {Debata, Biswaranjan and Priyadarshini, Rojalina and Mohapatra, Sudhir Kumar and Bedane, Tarikwa Tesfa},
volume = {21},
number = {2},
year = {2025},
month = {Jan},
pages = {413-423},
doi = {10.3844/jcssp.2025.413.423},
url = {https://thescipub.com/abstract/jcssp.2025.413.423},
abstract = {Tuberculosis (TB) is a major public health issue in India, contributing significantly to the global burden of respiratory diseases. This study introduces a Convolutional Neural Network (CNN)--based model for the early and cost-effective detection of TB using chest X-ray images. The proposed model, featuring 13 layers and automated hyperparameter selection, classifies images as infected or not infected. It is evaluated on three open datasets: Chest X-ray Masks and Labels, Tuberculosis X-ray (TB ×11 K), and Shenzhen. The model achieves an accuracy of 99.42% on the chest X-ray masks and label dataset, 99.27% on the TB ×11 K dataset, and 97.73% on the Shenzhen dataset, outperforming six existing models in terms of F1 score and precision. Unlike existing models that are tested on a single dataset, our model demonstrates consistent and robust performance across multiple datasets, highlighting its generalizability.},
journal = {Journal of Computer Science},
publisher = {Science Publications}
}