Image Segmentation Final Year Projects with Source Code

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

Image Segmentation 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 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.
  4. 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.
  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. 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.
  7. Classification of Liver Fibrosis From Heterogeneous Ultrasound Image
    This project studies how artificial intelligence can help doctors diagnose liver problems using ultrasound images. The researchers found that AI models work well on images similar to those they were trained on but perform worse on images from different machines. They also explored ways to reduce errors caused by differences between machines and improved classification by combining similar categories. The work highlights the need for AI that performs reliably across different ultrasound devices.
  8. 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.
  9. Exudate Regeneration for Automated Exudate Detection in Retinal Fundus Images
    This project focuses on detecting early signs of diabetic eye disease from retinal images. It creates a method to generate and highlight disease spots using a small set of open-source images. A custom neural network is developed to classify these spots accurately. The system performs very well, achieving perfect results on the test data.
  10. Loop Residual Attention Network for Automatic Segmentation of COVID-19 Chest X-Ray Images
    This project focuses on using artificial intelligence to analyze chest X-rays for COVID-19 detection. The researchers developed a new method that can accurately identify infected areas, even when their size or location varies. The approach improves how the system understands both the position and details of the infection in the X-ray images. Tests on public datasets show it works better and more reliably than existing methods.
  11. 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.
  12. 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.
  13. 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.
  14. 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.
  15. Lung-RetinaNet Lung Cancer Detection Using a RetinaNet With Multi-Scale Feature Fusion and Context Module
    This project focuses on developing an automated system to detect lung tumors quickly and accurately. It uses a deep learning model called Lung-RetinaNet, which combines features from multiple layers to improve tumor detection, especially for small tumors. The system achieves very high accuracy and outperforms existing methods, making early diagnosis faster and more reliable.
  16. Malaria Disease Cell Classification With Highlighting Small Infected Regions
    This project uses deep learning to detect malaria from images of red blood cells. The researchers created a method that focuses on the small infected regions in the cells, similar to how humans highlight important information. Their approach improved the accuracy of malaria detection on a public dataset to 97.2%, which is higher than standard models. The study shows that focusing on key areas in the images helps the neural network learn better.
  17. 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.
  18. 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.
  19. Skin Medical Image Captioning Using Multi-Label Classification and Siamese Network
    This project develops a system that can automatically describe skin images using simple sentences. It uses multiple machine learning models to identify skin features, match keywords, and relate them to everyday language descriptions. The system achieved very high accuracy and can help teach dermatology, especially in hospitals or schools with limited resources. It makes learning skin diagnosis easier and supports practical training for medical students.
  20. 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.
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