X-RAY SEGMENTATION BASED TUBERCULOSIS DETECTION USING DEEP LEARNING

Parvathi.Sankranti; Mr. Yerrabathana Guravaiah

X-RAY SEGMENTATION BASED TUBERCULOSIS DETECTION USING DEEP LEARNING

Authors: Parvathi.Sankranti, Mr. Yerrabathana Guravaiah

Unique Paper ID: 177601
Volume: 11
Issue: 12
PageNo: 1015-1018

Keywords: X-ray images Deep Learning Tuberculosis Detection and Accuracy.

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.

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 12
PageNo: 1015-1018

X-RAY SEGMENTATION BASED TUBERCULOSIS DETECTION USING DEEP LEARNING

Available:https://ijirt.org/Article?manuscript=177601

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