International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Automatic Detection of Tuberculosis from Chest X-Rays |
|---|---|
| ABSTRACT | Tuberculosis (TB) is one of the most dangerous infectious diseases in the world and early detection is very important for saving human lives. Manual reading of chest X-rays by radiologists is time consuming and not always available in remote areas. This project is made to reduce this problem by building an automatic system for detecting TB from chest X-ray images using deep learning. The system uses a convolutional neural network model to classify X-ray images as TB-positive or TB-negative. Pre-trained models like VGG16 and ResNet50 are used with transfer learning to improve accuracy on limited medical data. Image preprocessing techniques like histogram equalization, resizing, and data augmentation are applied before training. Flask is used for building the web interface and MongoDB is used for storing patient records and prediction results. The system can automatically analyze uploaded X-ray images and give prediction result with confidence score. After testing, the system shows high accuracy and can help doctors and health workers in early diagnosis of tuberculosis. |
| AUTHOR | CHANDANA H O, DR. RAGHAVENDRA S P MCA Student, Jawaharlal Nehru New College of Engineering, Shivamogga, Karnataka, India Assistant Professor, Dept. of MCA, Jawaharlal Nehru New College of Engineering, Shivamogga, Karnataka, India |
| VOLUME | 184 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1405106 |
| pdf/106_Automatic Detection of Tuberculosis from Chest X-Rays.pdf | |
| KEYWORDS | |
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