Abstract: Machine Learning has been potentially contributing towards smart diagnosis in the medical domain for more than a decade with a target towards achieving higher accuracy in detection and classification. However, from the perspective of medical image processing, the contribution of machine learning towards segmentation is not been much to find in recent times. The proposed study considers a use case of Tuberculosis detection and classification from chest x-rays where a unique machine learning approach of Convolution Neural Network is adopted for segmentation of lung images from CXR. A computational framework is developed that performs segmentation, feature extraction, detection, and classification. The proposed system's study outcome is analyzed with and without segmentation over existing machine learning models to exhibit 99.85% accuracy, which is the highest score to date in contrast to existing approaches found in the literature. The study outcome based on the comparative analysis exhibits the effectiveness of the proposed system.

Roopa N K and Mamatha G S, “An Add-on CNN based Model for the Detection of Tuberculosis using Chest X-ray Images” International Journal of Advanced Computer Science and Applications(IJACSA), 14(3), 2023. http://dx.doi.org/10.14569/IJACSA.2023.0140313

@article{K2023,
title = {An Add-on CNN based Model for the Detection of Tuberculosis using Chest X-ray Images},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140313},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140313},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {3},
author = {Roopa N K and Mamatha G S}
}