Comparative Study of various techniques using Deep Learning for Brain Tumor Detection

One of the life-threatening disease affecting the brain is the cancer of the brain. Detection of the tumor at an early stage becomes essential in order to save life's. One of the techniques used for detection of brain tumor is based on medical images. Deep learning is being used in order to detect brain tumor. Using deep learning techniques, has shown reduction of error in human early diagnosis of the disease. Especially, brain tumor diagnosis requires high accuracy, where minute errors in diagnosis may lead to complications. In medical image processing, brain tumor disclosure remains a demanding job. The image of the brain is complicated to detect the tumor. Several noises moreover delay affects the image accuracy. Image segmentation and MRI (magnetic resonance imaging) techniques have become a helpful medical diagnostic tool for the examination of the brain and other medical images. Image segmentation is an influential area of medical image processing. It is applied to bring out the different roles from medical images like MRI, CT scan, including Mammography, etc. In this paper, we presented a systematic review of brain disease using deep learning techniques. The investigation and comparative analysis of recent knowledge correlated with brain disorder detection using deep learning techniques is considered in this review. The outcome of this paper states the various research gaps identified from the literature review.