Paper Details

A brain tumor is a collection, mass, or growth of abnormal cells in the brain. It occurs due to abnormalformation within the brain. Recently it is becoming a major cause of death of many people, so to save a life, immediatedetection and proper treatment need to be done. Determination of tumor extent is a major challenge in treatmentplanning of brain tumor. Diagnosis and treatment of brain tumors require a delicate segmentation of brain tumors, as aprerequisite. Non-invasive technique like MRI has emerged as a frontline diagnostic tool for brain tumor diagnosis.Manual segmentation of brain tumor extent from MRI volumes is complicated and time-consuming task due to complexityand variance of tumors. This conventional method relies on physician’s experience and knowledge which conventionallycost a lot of precious time. In this context, a fully automatic segmentation method is needed for an effective brain tumordetection assisting the doctor in diagnosis and treatment planning. Recently various techniques have been developed forautomatic detection of brain tumor. Deep learning method have been proved to be popular compared to other state of theart method as it achieves promising result. In this paper, we have presented several works performed by the differentauthors for detecting the brain tumor using deep learning techniques such as DNN and CNN, and also highlight theproposed method of various authors by summarizing the papers.

Mohd Arshad Siddique, Arham Ansari, Mohd Asif Siddique, Dr. Mohd Riyazoddin Siddiqui, “Brain Tumor Segmentation And Detection Using Deep Learning”, International Journal of Advance Engineering and Research Development (IJAERD), Vol. 7, Issue 4, pp. 1-5, April 2020.