Chest X-ray Images Final Year Projects with Source Code
Chest X-ray Images Final Year Projects for BE, BTech, ME, MSc, MCA and MTech final year engineering students. These Chest X-ray Images projects give practical experience and help complete final-year submissions. All projects follow IEEE standards and each project includes source code, project thesis report, presentation, project execution and explanation.
Chest X-ray Images Final Year Projects
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A Systematic Review on Federated Learning in Medical Image Analysis
This project reviews how Federated Learning (FL) is used for analyzing medical images while keeping patient data private. The authors collected and studied research articles to understand how FL models perform compared to traditional methods. They summarized the current methods, results, challenges, and suggested directions for future research. Overall, it gives a clear picture of FL’s role in privacy-preserving medical AI. -
An Automated Chest X-Ray Image Analysis for Covid-19 and Pneumonia Diagnosis using Deep Ensemble Strategy
This project develops an AI-based system to detect Covid-19 and pneumonia from chest X-ray images. It uses advanced deep learning models to analyze images and identify diseases more accurately than traditional methods. The system improves image data with techniques like rotation and flipping and combines multiple models to make reliable predictions. Experiments show it achieves around 97% accuracy, helping doctors make faster and better treatment decisions. -
An Improved Densenet Deep Neural Network Model for Tuberculosis Detection Using Chest X-Ray Images
This project focuses on detecting tuberculosis (TB) from chest X-ray images using a new deep learning model called CBAMWDnet. The model combines advanced techniques to better understand important features in the images. Tests on large datasets show it is very accurate and performs better than many existing models. This approach can help doctors diagnose TB earlier and more reliably. -
Loop Residual Attention Network for Automatic Segmentation of COVID-19 Chest X-Ray Images
This project focuses on using artificial intelligence to analyze chest X-rays for COVID-19 detection. The researchers developed a new method that can accurately identify infected areas, even when their size or location varies. The approach improves how the system understands both the position and details of the infection in the X-ray images. Tests on public datasets show it works better and more reliably than existing methods. -
Assessing Inter-Annotator Agreement for Medical Image Segmentation
This study looks at how differences between medical experts can affect the training of AI systems for analyzing medical images. It measures how consistently multiple experts label the same lesions or abnormalities. The researchers use visual maps, statistical coefficients, and an algorithm called STAPLE to check agreement and create accurate ground truth for AI training. They tested their methods on cervical and chest X-ray images to show how combining different measures improves reliability. -
Classification and Localization of Multi-Type Abnormalities on Chest X-Rays Images
This project uses deep learning to analyze chest X-ray images and detect lung problems, including COVID-19. It develops models that can not only classify different diseases but also show where they are in the lungs. By combining several detection models, it improves accuracy compared to single models. The system can help doctors make faster and more reliable diagnoses. -
Fusion of Textural and Visual Information for Medical Image Modality Retrieval Using Deep Learning-Based Feature Engineering
This project focuses on helping doctors quickly understand medical images by identifying the type of imaging technique used, like X-rays or skin scans. The researchers combine visual patterns and texture details from images using deep learning to extract important features. They then merge these features to improve accuracy in classifying the images. Their method shows high precision and recall, making medical image analysis faster and more reliable.
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Chest X-ray Images Project Synopsis & Presentation
Final Year Projects helps prepare Chest X-ray Images project synopsis, including problem statement, objectives, existing system, disadvantages, proposed system, advantages and research motivation. We provide PPT slides, tutorials, and full documentation for presentations.
Chest X-ray Images Project Thesis Writing
Final Year Projects provides thesis writing services for Chest X-ray Images projects. We help BE, BTech, ME, MSc, MCA and MTech students complete their final year project work efficiently.
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We accept Word, RTF, and LaTeX formats. Every paper is reviewed to meet IEEE and publication standards, improving acceptance chances. Our guidance ensures that students produce high-quality, publication-ready research papers.
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