Brain Tumour Segmentation Using Convolutional Neural Network with Tensor Flow

Abstract

Introduction: The determination of tumour extent is a major challenging task in brain tumour planning and<br />quantitative evaluation. Magnetic Resonance Imaging (MRI) is one of the non-invasive technique has emanated as<br />a front- line diagnostic tool for brain tumour without ionizing radiation. Objective: Among brain tumours, gliomas<br />are the most common aggressive, leading to a very short life expectancy in their highest grade. In the clinical practice<br />manual segmentation is a time consuming task and their performance is highly depended on the operator's experience.<br />Methods: This paper proposes fully automatic segmentation of brain tumour using convolutional neural network.<br />Further, it uses high grade gilomas brain image from BRATS 2015 database. The suggested work accomplishes<br />brain tumour segmentation using tensor flow, in which the anaconda frameworks are used to implement high level<br />mathematical functions. The survival rates of patients are improved by early diagnosis of brain tumour. Results: Hence,<br />the research work segments brain tumour into four classes like edema, non-enhancing tumour, enhancing tumour and<br />necrotic tumour. Brain tumour segmentation needs to separate healthy tissues from tumour regions such as advancing<br />tumour, necrotic core and surrounding edema. This is an essential step in diagnosis and treatment planning, both of<br />which need to take place quickly in case of a malignancy in order to maximize the likelihood of successful treatment.