BRAIN MRI SEGMENTATION MODELLING USING DEEP LEARNING

BRAIN MRI SEGMENTATION MODELLING USING DEEP LEARNING

Samiksha Atkari, Sakshi Walke, Tannu Ramteke, Kautika Pandhare, Prem Hirulkar, Dr.(Mrs.) S.P.Washimkar 

Priyadarshini College of Engineering, Nagpur

Department of Electronics & Telecommunication Engineering

Introduction

The human body is made up of many organs and brain is the most critical and vital organ of them all. One of the common reasons for dysfunction of brain is brain tumor. A tumor is nothing but excess cells growing in an uncontrolled manner

Currently, doctors locate the position and the area of brain tumor by looking at the MR Images of the brain of the patient manually. This results in inaccurate detection of the tumor and is considered very time consuming.

The brain tumor detection and classification system is available so that it can be diagnosed at early stages. Cancer classification is the most challenging tasks in clinical diagnosis.

This project deals with such a system, which uses computer, based procedures to detect tumor blocks and classify the type of tumor using Convolution Neural Network Algorithm for MRI images of different patients. Different types of image processing techniques like image segmentation, image enhancement and feature extraction are used for the brain tumor detection in the MRI images of the cancer-affected patients. Detecting Brain tumor using Image Processing techniques its involves the four stages is Image Pre-Processing, Image segmentation, Feature Extraction, and Classification. Image processing and neural network techniques are used for improve the performance of detecting and classifying brain tumor in MRI images.