GitHub / vishal815 / Deep-Learning-Models-for-3D-MRI-based-Brain-Tumor-Segmentation-using-Seg-Net-V--Net-and-U-Net
This project focuses on the segmentation of brain tumors in 3D MRI images using Convolutional Neural Network (CNN) models. The research compares the performance of SegNet, V-Net, and U-Net architectures for brain tumor segmentation and evaluates them based on complexity, training time, and segmentation accuracy.
JSON API: http://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/vishal815%2FDeep-Learning-Models-for-3D-MRI-based-Brain-Tumor-Segmentation-using-Seg-Net-V--Net-and-U-Net
PURL: pkg:github/vishal815/Deep-Learning-Models-for-3D-MRI-based-Brain-Tumor-Segmentation-using-Seg-Net-V--Net-and-U-Net
Stars: 5
Forks: 2
Open issues: 0
License: mit
Language: Jupyter Notebook
Size: 16.9 MB
Dependencies parsed at: Pending
Created at: about 1 year ago
Updated at: 3 months ago
Pushed at: about 1 year ago
Last synced at: 5 days ago
Topics: 3d-cnn-model, 3d-segmentation, 4d-database, capstone-project, cnn, deep-learning, end-to-end, image-segmentation, novel, seg-net, tensorflow-keras, u-net, v-net, vishal-lazrus, vishallazrus