Computed Tomography Images Final Year Projects for BE, BTech, ME, MSc, MCA and MTech final year engineering students. These Computed Tomography 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.

Computed Tomography Images Final Year Projects

Automatic Liver Cancer Detection Using Deep Convolution Neural Network

This project focuses on automatically detecting liver cancer from CT scans. It uses a new method called ESP-UNet to accurately separate the liver from the rest of the image, avoiding errors in segmentation. After that, a lightweight deep learning model analyzes the segmented liver to detect cancer. The method shows better results than previous approaches in terms of accuracy and reliability.
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.
CT Lung Nodule Segmentation A Comparative Study of Data Preprocessing and Deep Learning Models

This project focuses on improving early detection of lung cancer using computer programs. It uses CT scans to identify lung nodules, which can be cancerous. The researchers tested different deep learning models to automatically find and segment these nodules. They found that one model, TransUNet, gave the most accurate results when the scans were processed by focusing on the nodule regions.
Intracranial Haemorrhage Diagnosis Using Willow Catkin Optimization With Voting Ensemble Deep Learning on CT Brain Imaging

This project focuses on automatically detecting brain bleeding from CT scans using artificial intelligence. It creates a smart system that learns important patterns in images to classify different types of bleeding. The model combines multiple AI techniques to improve accuracy and speed. This helps doctors diagnose and treat patients faster while reducing manual effort.

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At Final Year Projects, we provide complete guidance for Computed Tomography Images 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.

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Computed Tomography Images Project Synopsis & Presentation

Final Year Projects helps prepare Computed Tomography 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.

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Computed Tomography Images Final Year Projects with Source Code