Computed Tomography Projects for M.E, M.Tech, Masters, MS abroad, and PhD students. These Computed Tomography ieee projects are implemented with future work and extension for final year project submission with research paper publishing. These research projects guide final year students to learn, practice, and complete their academic submissions successfully. Each project includes complete source code, project report, PPT, a tutorial, documentation, and a research paper.
Latest Computed Tomography Projects
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BucketAugment: Reinforced Domain Generalisation in Abdominal CT Segmentation
This project focuses on improving how computers identify organs in CT scans, like kidneys and livers. It introduces a method called BucketAugment that helps neural networks work well on new data from different hospitals. The method uses a smart learning process to find the best way to adjust images during training. Overall, it makes medical image analysis more reliable and flexible across different datasets. -
GMILT: A Novel Transformer Network That Can Noninvasively Predict EGFR Mutation Status
This project uses computer analysis of CT scans to predict a gene mutation called EGFR in lung cancer patients. It can highlight the most suspicious area in the tumor, helping doctors take more accurate biopsies. The method uses a special deep learning model that learns from both the images and tumor features. Tests showed it works better than older techniques and can help guide treatment decisions. -
Precision and Robust Models on Healthcare Institution Federated Learning for Predicting HCC on Portal Venous CT Images
This project focuses on detecting liver cancer using CT scans. It uses smart computer programs that learn from many hospitals’ data without sharing patient details. The system can accurately find the liver and tumor areas in images. It aims to make cancer detection faster, safer, and more reliable for doctors. -
Uncertainty Estimation in Unsupervised MR-CT Synthesis of Scoliotic Spines
This project focuses on teaching a computer to convert MRI scans of spines into CT-like images. It also measures how confident the computer is about its predictions. By looking at uncertainties, the model can better separate bones from soft tissues. This helps doctors and researchers trust the results more when no expert labels are available. -
BucketAugment Reinforced Domain Generalisation in Abdominal CT Segmentation
This project focuses on improving how computers identify organs in CT scans. The researchers created a new method called BucketAugment. It helps deep learning models work well on medical images from different hospitals and machines. This method makes organ segmentation more accurate without major changes to existing systems. -
Data-Driven Gradient Regularization for Quasi-Newton Optimization in Iterative Grating Interferometry CT Reconstruction
This project focuses on improving breast cancer imaging using a new CT technique called GI-CT. The researchers developed a smart algorithm named GradReg that makes CT images clearer and less noisy. It works well for both conventional and GI-CT scans and can help reduce the radiation dose. Overall, it makes it easier to detect details in breast images. -
Supplemental Transmission Aided Attenuation Correction for Quantitative Cardiac PET
This project develops a new method to improve heart PET scans. It uses a small external source together with the patient’s own signals to correct image errors. The approach does not rely on prior images or assumptions. Tests show it produces accurate images with low bias and less noise, making heart PET scans more reliable for patients. -
Weakly-Supervised Segmentation-Based Quantitative Characterization of Pulmonary Cavity Lesions in CT Scans
This project developed an artificial intelligence system to detect and measure lung cavity lesions from CT scans. The system can automatically find and outline the affected areas. It also calculates important features like size and thickness. This tool can help doctors diagnose, monitor, and track treatment of lung lesions more quickly and accurately.
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