Medical Imaging Final Year Projects with Source Code

Byadmin Final Year Projects

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

Medical Imaging Final Year Projects

  1. AMSeg A Novel Adversarial Architecture Based Multi-Scale Fusion Framework for Thyroid Nodule Segmentation
    This project focuses on automatically detecting and outlining thyroid nodules in ultrasound images. The researchers developed a new deep learning method that can identify nodule boundaries even when the tissue is blurry or uneven. Their system, called AMSeg, performs better than existing methods and can replace manual segmentation. This could make thyroid disease diagnosis faster and more accurate in clinical settings.
  2. An Efficient Transfer and Ensemble Learning Based Computer Aided Breast Abnormality Diagnosis System
    This project focuses on improving early detection of breast cancer using computer-based tools. The researchers used advanced deep learning models, including EfficientNet, to analyze mammogram images. They combined multiple models together to make predictions more accurate and reliable. Their system achieved high accuracy in identifying both the type of abnormality and the disease itself.
  3. 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.
  4. 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.
  5. Biomedical Image Analysis for Colon and Lung Cancer Detection Using Tuna Swarm Algorithm With Deep Learning Model
    This project focuses on creating an AI system to detect colon and lung cancer from biomedical images. The system uses advanced image processing and deep learning techniques to analyze patient scans quickly and accurately. It combines smart algorithms to improve feature extraction and classification of cancer cells. The results show it can detect cancer with very high accuracy, reaching over 99%.
  6. EMED-UNet An Efficient Multi-Encoder-Decoder Based UNet for Medical Image Segmentation
    This project improves medical image segmentation by making the popular U-Net model faster and lighter. The researchers created a new version called EMED-UNet, which uses multiple encoders and decoders to capture features more effectively. It works well on different medical imaging datasets while using much less memory and computing power. Overall, it is accurate, efficient, and easier to deploy in real-time applications.
  7. An Improved Medical Image Compression Method Based on Wavelet Difference Reduction
    This project develops a new method to compress large medical images efficiently without losing important details. It improves existing techniques by using color and spatial similarities to reduce data size while keeping high image quality. The method was tested on colorectal cancer slides and showed much better results than standard JPEG and wavelet-based methods. It can be used in mobile and web platforms to transmit medical images quickly and accurately.
  8. Brain Tumor Detection and Multi-Grade Segmentation Through Hybrid Caps-VGGNet Model
    This project focuses on improving brain tumor detection using medical images. It combines two neural network models, CapsNet and VGGNet, to create a hybrid system that can automatically identify and classify tumors. The model works well even with smaller datasets and was tested on high-quality brain tumor images. It achieved very high accuracy, correctly identifying almost all tumors.
  9. 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.
  10. Classification of Polyps in Endoscopic Images Using Self-Supervised Structured Learning
    This project develops a smart computer system to identify types of polyps in medical images. It uses a neural network that can teach itself to focus on the whole polyp, even with few labeled images. The system improves accuracy by learning from both medical and natural images before fine-tuning on polyps. The result is a more reliable classification of polyps as either hyperplastic or tubular adenoma.
  11. Computer Aided Diagnosis for Gastrointestinal Cancer Classification Using Hybrid Rice Optimization With Deep Learning
    This project aims to detect stomach and digestive cancers early using computer analysis of medical images. It cleans the images and then uses advanced AI models to learn important patterns. The system chooses the best settings automatically to improve accuracy. This helps doctors identify cancer sooner and make better treatment decisions.
  12. 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.
  13. Data Augmentation Based on Generative Adversarial Networks for Endoscopic Image Classification
    This project aims to help doctors detect digestive system diseases more easily using computer-based image analysis. The system trains several deep learning models to automatically classify diseases from endoscopy images. It also creates extra training images using generative models to improve accuracy. The final model shows strong and safe performance, reducing the workload on medical staff.
  14. Deep CleanerA Few Shot Image Dataset Cleaner Using Supervised Contrastive Learning
    This project focuses on improving the quality of medical images before they are used for AI diagnosis. It automatically removes noisy or unwanted parts of images using a learning model trained on only a few clean examples. The system learns to separate correct images from incorrect ones. After cleaning, the accuracy of disease classification improves significantly.
  15. Enhancing Breast Cancer Classification in Histopathological Images through Federated Learning Framework
    This study develops an automated system to detect breast cancer from medical images. It secures patient data by encrypting images and storing them safely using advanced machine learning techniques. The system then classifies the images using a deep learning model, which is optimized for better accuracy. Tests show the method is highly effective, achieving over 95% accuracy and reliability.
  16. 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.
  17. 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.
  18. 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.
  19. Looking Closer to the Transferability Between Natural and Medical Images in Deep Learning
    This study looks at how to improve medical image analysis using machine learning. It tests if knowledge from natural images can help in medical imaging. The researchers found that using natural images does not improve results much because medical images are very different. They also studied data enhancement techniques and found that these methods don’t transfer well from natural to medical images.
  20. Artificial Intelligence and Biosensors in Healthcare and Its Clinical Relevance A Review
    This project explores how artificial intelligence can use large amounts of medical data from sources like wearable sensors, medical images, and health records. It shows how AI can help with disease diagnosis, monitoring body signals, and delivering personalized treatments. The study also highlights new computing tools like cloud, GPUs, and edge devices that make this possible. Finally, it discusses challenges in handling medical data and the future of AI-driven healthcare.
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