Detection of Tuberculosis using X-Ray Based on Deep Learning

Registration ID: IJNRD_305595

Published ID: IJNRD2504396

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Mr. Vaibhav Rathod, Miss. Unnati Deshmukh, Mr. Om Kale, Miss. Priti Lahane, Prof. P. R. Nerkar

Tuberculosis Detection, Chest X-ray (CXR), Deep Learning, Convolutional Neural Networks (CNNs), VGG16, VGG19, and Block Attention Module (BAM). It also highlights Medical Image Analysis, Image Classification, Feature Extraction, Automated Diagnosis, and Computer-Aided Detection (CAD). The study incorporates an improved Canny Edge Detection method with Morphological Enhancement to enhance structural clarity. Keywords like Accuracy, Precision, Recall, and F1-Score are used to evaluate the model's performance.

Tuberculosis (TB) remains a critical public health concern, particularly in resource-limited regions where early and precise diagnosis is challenging due to the limitations of conventional methods such as sputum analysis and radiological interpretation. To address this, the present study proposes a novel deep learning-based approach for TB detection utilizing chest X-ray (CXR) images, capitalizing on the powerful feature extraction capabilities of two pre-trained convolutional neural networks, VGG16 and VGG19, both trained on the ImageNet dataset. By integrating these architectures with a Block Attention Module (BAM), the model effectively enhances its ability to capture spatial dependencies and concentrate on diagnostically significant regions within the images. Evaluated across four well-established public datasets, the proposed model demonstrates outstanding performance, achieving a near-perfect evaluation score of 0.9992 across key metrics including accuracy, precision, recall, and F1-score. Furthermore, the study introduces an improved Canny edge detection algorithm that incorporates local morphological contrast enhancement, substantially boosting the clarity of essential anatomical structures in CXR images. This enhancement not only facilitates superior feature localization but also significantly augments the overall diagnostic utility of the imaging process, underscoring the potential of the proposed system as a reliable tool for automated TB detection

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Mr. Vaibhav Rathod, Miss. Unnati Deshmukh, Mr. Om Kale, Miss. Priti Lahane, & Prof. P. R. Nerkar (April-2025). Detection of Tuberculosis using X-Ray Based on Deep Learning . INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 10(4), d622-d628. https://ijnrd.org/papers/IJNRD2504396.pdf

Volume 10 Issue 4, April-2025

Pages : d622-d628

Paper Reg. ID: IJNRD_305595

Published Paper Id: IJNRD2504396

Research Area: Science and Technology

Author Type: Indian Author

Country: Amravati, Maharashtra, India

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