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@article{177601,
        author = {Parvathi.Sankranti and Mr. Yerrabathana Guravaiah},
        title = {X-RAY SEGMENTATION BASED TUBERCULOSIS DETECTION USING DEEP LEARNING},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {1015-1018},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=177601},
        abstract = {Tuberculosis (TB) remains one of the most prevalent and life-threatening infectious diseases worldwide, particularly in low-resource regions. Chest X-ray imaging is a primary diagnostic tool for TB, but manual interpretation is often subject to variability and diagnostic delay. This research introduces an automated tuberculosis detection framework based on deep learning and X-ray image segmentation. The proposed system utilizes a U-Net-based architecture for lung region segmentation, followed by a convolutional neural network (CNN) for TB classification. By isolating the region of interest, the model improves detection accuracy and robustness. Experimental results on public chest X-ray datasets demonstrate the system’s high sensitivity, specificity, and classification accuracy. This approach has the potential to assist radiologists and healthcare providers by offering fast, scalable, and consistent TB diagnosis from X-ray scans.},
        keywords = {X-ray images, Deep Learning, Tuberculosis Detection, and Accuracy.},
        month = {May},
        }