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

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.
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.
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.
HarDNet and Dual-Code Attention Mechanism Based Model for Medical Images Segmentation

This project focuses on improving the accuracy of medical image analysis. The researchers designed a model that better identifies important features in images and separates them from the background. It uses special modules to speed up processing and highlight both position and detail information. Tests on different medical datasets showed the method works very well, helping doctors diagnose diseases faster and more accurately.
Learning From Multiple Expert Annotators for Enhancing Anomaly Detection in Medical Image Analysis

This project focuses on improving computer programs that detect problems in medical images, like X-rays. Normally, these programs learn from labels given by doctors, but different doctors may disagree, which lowers accuracy. The researchers developed a method to combine labels from multiple doctors and figure out the most likely “true” label. This makes the program more reliable and accurate at spotting abnormalities in medical images.
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.

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Medical Image Datasets Project Synopsis & Presentation

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Medical Image Datasets Final Year Projects with Source Code