U-Net For Segmentation Final Year Projects with Source Code

Byadmin Final Year Projects

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

U-Net For Segmentation Final Year Projects

  1. 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.
  2. Multi-Class Retinal Diseases Detection Using Deep CNN With Minimal Memory Consumption
    This project focuses on using machine learning to identify and classify eye diseases. The researchers designed a new neural network model that works efficiently without using too much memory. They tested it on a dataset containing 32 types of retinal diseases. The model performed very well, achieving 95% accuracy while managing resources better than previous methods.
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U-Net For Segmentation Project Synopsis & Presentation

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