Magnetic Resonance Imaging Final Year Projects with Source Code

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Magnetic Resonance Imaging Final Year Projects for BE, BTech, ME, MSc, MCA and MTech final year engineering students. These Magnetic Resonance 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.

Magnetic Resonance Imaging Final Year Projects

  1. A CNN-Model to Classify Low-Grade and High-Grade Glioma From MRI Images
    This project focuses on identifying how severe a brain tumor is using MRI images. It uses a light and fast deep learning model to classify tumors into low-grade or high-grade groups. The model is trained on public medical datasets and data from a local hospital. It shows very high accuracy compared to other popular deep learning models.
  2. A Nested Attention Guided UNet Architecture for White Matter Hyperintensity Segmentation
    This project focuses on improving the detection of White Matter Hyperintensity (WMH) in brain MRI scans, which is important for predicting recovery in stroke patients. The researchers developed a new deep learning method called NAUNet++ that uses attention mechanisms and atlas images to better identify WMH regions. Their approach produces more accurate and faster segmentation results than existing methods, helping doctors assess patient prognosis more reliably.
  3. A Review on Alzheimers Disease Through Analysis of MRI Images Using Deep Learning Techniques
    This project focuses on using brain MRI scans to detect Alzheimer’s disease early. It applies deep learning, especially convolutional neural networks, to analyze brain structures and identify signs of the disease. By examining the detailed tissue patterns, the method aims to improve accuracy in diagnosing Alzheimer’s. The study also reviews recent research and techniques showing how MRI segmentation helps in early detection.
  4. 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.
  5. Automated Segmentation of Brain Tumor MRI Images Using Deep Learning
    This project focuses on automatically identifying and separating brain tumors in MRI images. It uses advanced image processing and neural networks to remove unnecessary details and improve tumor detection. The method combines two deep learning models to make predictions more accurate and complete. Tests show it achieves very high accuracy, precision, and reliability in identifying different tumor regions.
  6. 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.
  7. DeepCurvMRI Deep Convolutional Curvelet Transform-Based MRI Approach for Early Detection of Alzheimers Disease
    This project aims to detect Alzheimer’s Disease early using MRI brain images. The researchers first enhanced the images and then trained a deep learning model to recognize patterns linked to different stages of the disease. The model learned these patterns with very high accuracy. This approach could help doctors identify Alzheimer’s much earlier and more reliably.
  8. Failure to Achieve Domain Invariance With Domain Generalization Algorithms An Analysis in Medical Imaging
    This project studies how well deep learning methods can handle new data that is different from what they were trained on. The researchers tested eight popular algorithms on medical images and regular photos. They found that all methods perform similarly, and none fully remove data-specific biases. The study shows that current techniques still struggle to generalize to unseen data, especially in important fields like medical imaging.
  9. Gated Deep Reinforcement Learning With Red Deer Optimization for Medical Image Classification
    This project focuses on improving brain tumor detection from MRI images using artificial intelligence. The researchers developed a deep learning model that automatically processes and classifies images, reducing errors and saving time. They used special filters and mathematical techniques to extract important features from the images. The model achieved very high accuracy of 98.8% when tested on brain tumor data.
  10. Improved Prostate Biparameter Magnetic Resonance Image Segmentation Based on Def-UNet
    This project focuses on improving the detection of the prostate and prostate cancer from medical images. It combines a special type of convolution called deformable convolution with the U-Net model to better capture the changing shapes and sizes of the prostate. The method adapts to the features in the images, making segmentation more accurate. It also uses a training approach that transfers knowledge from healthy prostate images to cancer images, improving results even with limited data.
  11. Interpretation of Magnetic Resonance Images of Temporomandibular Joint Disorders by Using Deep Learning
    This study uses deep learning to automatically detect Temporomandibular Disorder (TMD) from MRI scans. Researchers collected over 2500 images from 200 patients and tested several advanced neural network models to classify them. The performance of these models was measured using accuracy and other medical metrics. The results show that deep learning can successfully assist in diagnosing TMD.
  12. Leveraging Brain MRI for Biomedical Alzheimers Disease Diagnosis Using Enhanced Manta Ray Foraging Optimization Based Deep Learning
    This project focuses on improving the diagnosis of Alzheimer’s disease using brain MRI scans. It uses deep learning to automatically analyze images and extract important features, reducing the need for manual input from experts. The method combines a DenseNet model for feature extraction with an optimized neural network for classification. Tests show that this approach gives more accurate results than existing techniques.
  13. Oppositional Jellyfish Search Optimizer With Deep Transfer Learning Enabled Secure Content-Based Biomedical Image Retrieval
    This project focuses on securely storing and retrieving medical images like X-rays, MRIs, and CT scans. It uses advanced deep learning techniques to extract important features from the images. The system encrypts images to keep them safe while allowing accurate searching and matching. Tests show that this method works better than existing approaches.
  14. A Deep Learning-Based Brain Age Prediction Model for Preterm Infants via Neonatal MRI
    This project develops a deep learning model called BAPNET to predict the brain age of premature infants using MRI scans. It helps estimate brain maturity quickly and accurately, reducing reliance on doctors’ manual assessments. The model learns from a large dataset of infant brain images and highlights important brain regions involved in development. This can support doctors in understanding brain growth and planning early interventions for preterm infants.
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