Automated Brain Tumor Detection and Brain MRI Classification Using Artificial Neural Network - A Review

Kalpana U. Rathod, Y. D. Kapse

Abstract: Magnetic resonance imaging (MRI) is an important imaging technique used in the detection of brain tumor. Brain tumor is one of the most dangerous diseases occurring among the children and adults. It is apparent that of chances survival of patient could be expanded if tumor is identified at its initial stage. Manual classification of brain tumor is time devastating and bestows ambiguous results. Automatic image classification is emergent thriving research area in medical field. Brain MRI plays a very important role for radiologists to diagnose and treat brain tumor patients. In this paper we present an overview of the current research being carried out using the neural network techniques and Tissue Segmentation Techniques for the diagnosis of brain tumor. The goal of this study is to understand use of neural networks in classification of brain MRI image and use of tissue Segmentation techniques including Thresholding, Region Based Segmentation and Edge Based Segmentation in detection of tumor in brain MRI image.

Keywords: MRI Brain Image, GLCM, FBNN, Support Vector Machine SVM, Fuzzy C-Means

How to Cite?: Kalpana U. Rathod, Y. D. Kapse, "Automated Brain Tumor Detection and Brain MRI Classification Using Artificial Neural Network - A Review", Volume 5 Issue 7, July 2016, International Journal of Science and Research (IJSR), Pages: 175-179, https://www.ijsr.net/getabstract.php?paperid=ART2016191, DOI: https://dx.doi.org/10.21275/ART2016191