U-Net Architecture Final Year Projects with Source Code
U-Net Architecture Final Year Projects for BE, BTech, ME, MSc, MCA and MTech final year engineering students. These U-Net Architecture 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 Architecture Final Year Projects
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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. -
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. -
Interpretation of Magnetic Resonance Images of Temporomandibular Joint Disorders by Using Deep Learning
This study uses deep learning to automatically detect Temporomandibular Disorder (TMD) from MRI scans. Researchers collected over 2500 images from 200 patients and tested several advanced neural network models to classify them. The performance of these models was measured using accuracy and other medical metrics. The results show that deep learning can successfully assist in diagnosing TMD.
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U-Net Architecture Project Synopsis & Presentation
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