{"repo":"angadbajwa23/Segmentation-of-2D-Brain-MR-Images-using-Deep-Neural-Architectures","free":true,"listed":false,"github":"https://github.com/angadbajwa23/Segmentation-of-2D-Brain-MR-Images-using-Deep-Neural-Architectures","clone":"git clone https://github.com/angadbajwa23/Segmentation-of-2D-Brain-MR-Images-using-Deep-Neural-Architectures.git","description":"Image Segmentation using U-Net, U-Net with skip connections and M-Net architectures","language":"Jupyter Notebook","stars":13,"topics":["u-net","imagesegmentation","dice-coefficient","data-visualization","m-net","resnet","brain-tumor-segmentation","computer-vision"],"license":null,"category":"machine-learning","readme_excerpt":"Brain-Image-Segmentation Segmentation of brain tissues in MRI image has a number of applications in diagnosis, surgical planning, and treatment of brain abnormalities. However, it is a time-consuming task to be performed by medical experts. In addition to that, it is challenging due to intensity overlap between the different tissues caused by the intensity homogeneity and artifacts inherent to MRI. Tominimize this effect, it was proposed to apply histogram based preprocessing. The goal of this project was to develop a robust and automatic segmentation of the human brain. To tackle the problem, I have used a Convolutional Neural Network (CNN) based approach. U-net is one of the most commonly used and best-performing architecture in medical image segmentation. This moodel consists of the 2-D implementation of the U-Net.The performance was evaluated using Dice Coefficient (DSC). Dataset This model was built for the following dataset: https://figshare.com/articles/brain tumor dataset/1512427 3064 T1-weighted contrast-inhanced images with three kinds of brain tumor are provided in the dataset.The three types of tumor are 1.Glioma 2.Pituitary Tumor 3.Meningioma Model Architecture The first half of the U-net is effectively a typical convolutional neural network like one would construct for an image classification task, with successive rounds of zero-padded ReLU-activated convolutions and ReLU-activated max-pooling layers. Instead of classification occurring at the \"bottom\" of the U,","default_branch":null,"files":null,"tree":[],"storefront":"/r/angadbajwa23","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/angadbajwa23/Segmentation-of-2D-Brain-MR-Images-using-Deep-Neural-Architectures/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}