X-Ray Imaging Final Year Projects for BE, BTech, ME, MSc, MCA and MTech final year engineering students. These X-Ray Imaging 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.

X-Ray Imaging Final Year Projects

Application of X-Ray Imaging and Convolutional Neural Networks in the Prediction of Tomato Seed Viability

This project focuses on predicting whether tomato seeds will grow successfully without damaging them. The researchers used X-ray images of seeds to check their internal structure. They created two prediction models: one based on image analysis and another using a type of artificial intelligence called a convolutional neural network (CNN). The CNN model was more accurate, achieving 86% accuracy, showing that this method can help farmers and scientists test seed quality quickly and safely.
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.
Conditional Generative Adversarial Network Model for Conversion of 2 Dimensional Radiographs into 3 Dimensional Views

This project develops a method to convert 2-D medical images like X-rays into 3-D views. It uses a specialized deep learning model that can show the organ from all angles. The system cleans and standardizes the images before processing, and it is designed to work even with noisy or unclear inputs. Tests on real hospital data show that the generated 3-D images preserve important details and match the quality of the original scans.
Multi-View Computed Tomography Network for Osteoporosis Classification

This study focuses on detecting early bone loss conditions, like osteopenia and osteoporosis, from CT scans. The researchers developed a new deep learning model called MVCTNet, which uses two images from a CT scan to automatically identify these conditions. Their approach avoids manual image cropping and improves accuracy compared to previous methods. Tests on nearly 3,000 patients’ CT images show that the model performs well and could help with earlier and easier diagnosis.

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At Final Year Projects, we provide complete guidance for X-Ray Imaging IEEE projects for BE, BTech, ME, MSc, MCA and MTech students. We assist at every step from topic selection to coding, report writing, and result analysis.

Our team has over 10 years of experience guiding students in Computer Science, Electronics, Electrical, and other engineering domains. We support students across India, including Hyderabad, Mumbai, Bangalore, Chennai, Pune, Delhi, Ahmedabad, Kolkata, Jaipur and Surat. International students in the USA, Canada, UK, Singapore, Australia, Malaysia, and Thailand also benefit from our expert guidance.

X-Ray Imaging Project Synopsis & Presentation

Final Year Projects helps prepare X-Ray Imaging 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.

X-Ray Imaging Project Thesis Writing

Final Year Projects provides thesis writing services for X-Ray Imaging projects. We help BE, BTech, ME, MSc, MCA and MTech students complete their final year project work efficiently.

All theses are checked with plagiarism check tools to guarantee originality and quality. Fast-track services are available for urgent submissions. Hundreds of students have successfully completed their projects and theses with our support.

X-Ray Imaging Research Paper Support

We offer complete support for X-Ray Imaging research papers. Services include writing, editing, and proofreading for journals and conferences.

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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X-Ray Imaging Final Year Projects with Source Code