U-Net Network Final Year Projects for BE, BTech, ME, MSc, MCA and MTech final year engineering students. These U-Net Network 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.

U-Net Network Final Year Projects

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.
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.
Facial Expression Transfer Based on Conditional Generative Adversarial Networks

This project focuses on transferring facial expressions from one face to another using advanced computer vision. It uses a special neural network model that combines key facial features from a source and target face. The model creates realistic images that keep the target person's identity while showing the new expression. Experiments show it works better and faster than previous methods.
Road Crack Detection Using Deep Neural Network Based on Attention Mechanism and Residual Structure

This project focuses on detecting cracks on roads to improve maintenance and safety. The researchers created a new deep learning model called AR-UNet, which uses attention modules to better capture both small and large crack details. Their method improves the accuracy and completeness of crack detection compared to existing models. They tested it on multiple datasets and made the code publicly available.

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U-Net Network Project Synopsis & Presentation

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U-Net Network Final Year Projects with Source Code