Design And Implementation Of Brain Tumor Detection

Analyzing brain tumor with no human intervention is considered as a vital area of research. However, this can be achieved using convolutional neural networks (CNNs). They have performed exceptionally well in solving computer vision problems and many others such as visual object recognition, detection and segmentation. It is used in detecting the brain tumor by optimizing the brain images using segmentation algorithms which are highly resilient towards noise and cluster size sensitivity problems with automatic region of Interest (ROI) detection. One of the main reasons choosing CNNs is due to its high accuracy and it is not necessary to perform manual feature extraction in these networks.It is not an easy task to detect the brain tumour and accurately identify the type. CCNs performance is better than others because of its wide usage in recognising images. Brain Tumor segmentation is one of the most crucial and arduous tasks in the terrain of medical image processing as a human-assisted manual classification can result in inaccurate prediction and diagnosis. Moreover, it is an aggravating task when there is a large amount of data present to be assisted. Brain tumors have high diversity in appearance and there is a similarity between tumor and normal tissues and thus the extraction of tumor regions from images becomes unyielding

KEYWORDS:  convolutional neural networks, detection, segmentation, automatic region of Interest (ROI), Brain Tumor.