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

A Deep Ensemble Learning-Based CNN Architecture for Multiclass Retinal Fluid Segmentation in OCT Images

This project uses eye scan images to detect and outline fluid-filled cysts inside the retina. It trains a deep learning model to automatically find these cysts, which normally takes doctors a lot of time to do by hand. The system can identify different types of cysts and helps doctors understand eye diseases better. It also performs better than many existing methods.
A VAN-Based Multi-Scale Cross-Attention Mechanism for Skin Lesion Segmentation Network

This project focuses on improving medical image segmentation, which helps computers identify regions like tumors in medical scans. Traditional methods using neural networks struggle to capture both small details and overall structures. The researchers combined ideas from Transformers and visual attention networks to create a new model called M-VAN Unet. This model uses special attention methods to better learn detailed and global features, and experiments show it performs better than existing methods.
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.
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.
Assessing Inter-Annotator Agreement for Medical Image Segmentation

This study looks at how differences between medical experts can affect the training of AI systems for analyzing medical images. It measures how consistently multiple experts label the same lesions or abnormalities. The researchers use visual maps, statistical coefficients, and an algorithm called STAPLE to check agreement and create accurate ground truth for AI training. They tested their methods on cervical and chest X-ray images to show how combining different measures improves reliability.
BSANet High-Performance 3D Medical Image Segmentation

This project focuses on improving medical image analysis, especially for tasks like brain tumor and organ segmentation. It introduces BSANet, a 3D network that can better understand images by focusing on important areas and combining information at different scales. This helps the system capture more details and make more accurate predictions. The model is tested on standard medical datasets and shows strong performance.
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.
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.
Medical Image Segmentation Based on Transformer and HarDNet Structures

This project improves medical image segmentation, which helps doctors detect diseases more accurately. It uses a new network with two encoders to capture both local details and overall image features. A special fusion module combines information from different layers to boost accuracy. Tests on several medical datasets show better results in identifying disease areas, helping in early diagnosis and treatment.
Medical Ultrasound Image Segmentation With Deep Learning Models

This project focuses on improving the analysis of medical ultrasound images. The researchers created a new model called ConvTrans-Net that combines a transformer and a deep neural network. It helps accurately identify and segment lesion areas in ultrasound scans. The model showed high precision and recall, making it more effective than some existing methods.
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.
Application of Artificial Intelligence Methods in Carotid Artery Segmentation A Review

This project focuses on analyzing images of the carotid artery, a major blood vessel in the neck that supplies blood to the brain. It uses artificial intelligence to automatically identify and measure the artery and any plaques that may form. These measurements help predict the risk of stroke and guide treatment. The study reviews how AI methods can improve accuracy compared to traditional techniques and discusses current challenges in this field.

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Medical Image Segmentation Project Synopsis & Presentation

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Medical Image Segmentation Final Year Projects with Source Code