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shalabh147 / Brain-Tumor-Segmentation-and-Survival-Prediction-using-Deep-Neural-Networks

Star 171

Use of state of the art Convolutional neural network architectures including 3D UNet, 3D VNet and 2D UNets for Brain Tumor Segmentation and using segmented image features for Survival Prediction of patients through deep neural networks.

deep-neural-networks deep-learning medical-imaging segmentation dice-scores keras-tensorflow survival-models dice-coefficient brain-tumor-segmentation unet-3d cnn-segmentation vnet3d survival-prediction glioma mri-segmentation dice-loss brats-dataset aiformedicine unet-architecture
  • Updated Jul 24, 2020
  • Jupyter Notebook
Retina_Blood_Vessel_Segmentation

YoussefAboelwafa / Retina_Blood_Vessel_Segmentation

Star 6

The project involves using a dataset of 100 high-resolution retinal fundus images for blood vessel segmentation to aid in early detection of retinal pathologies | Implemented U-Net architecture from scratch, known for its efficiency in semantic segmentation with limited data | The final model achieved 86% IoU score | PyTorch Lightning

computer-vision cnn image-segmentation unet-architecture
  • Updated Aug 2, 2025
  • Python

Abdul-Moiz-Shehzad / Dual-Input-Attention-Guided-GAN-for-Joint-Low-Light-Image-Enhancement-and-Super-Resolution

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Dual-AttnGAN: A dual-input GAN for joint low-light image enhancement and 4× super-resolution, fusing wide-angle and zoomed images via asymmetric cross-attention.

deep-learning generative-adversarial-network super-resolution image-restoration total-variational-denoising tensorflow-implemention relativistic-gan low-light-image-enhancement unet-architecture focal-frequency-loss dual-input-fusion asymmetric-cross-attention dual-branch-encoder telephoto-imaging wide-angle-guidance
  • Updated Dec 2, 2025
  • Jupyter Notebook

Aiman-Wazir / ML_medical-image_SR

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This project is an AI-powered Medical Image Super-Resolution system designed to enhance the quality of low-resolution X-ray and MRI images using a U-Net deep learning architecture. Built with TensorFlow, Keras, OpenCV, and Flask, the system reconstructs high-resolution images while preserving important anatomical details.

ai deep-learning medical-imaging high-resolution unet-image-segmentation unet-architecture
  • Updated Aug 13, 2026
  • Jupyter Notebook

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