A study on brain tumor segmentation using convolution neural network

Computer vision is playing important role in the field of human health care. This role is growing day by day. The application of computer vision techniques in health care has one of the aim to reduce human judgement in diagnosis. Thus, human error in judgement may be reduced. Brain related diagnosis demands at most care and a minute error in judgment may be disastrous. This makes medical imaging very important field. Various imaging methods like CT Scans, X-Ray, and MRI are available but MRI is the most reliable and safe. Even the smallest aberrances in the human body can be identified using imaging techniques. More preferred contrast information about brain tissues is provided by Magnetic Resonance imaging (MRI). Image segmentation is an important problem in medical imaging, which involves separating the tumor and organisms out of the medical data. Machine learning (ML) has gained enormous application with innovation in hardware requirements for computing. Convolutional neural networks (CNN) is one of the most effective techniques in ML. CNN has find applications in almost every field of research. CNN also find effective applications in brain MRI segmentation. In this paper, we present a study on CNN based MRI segmentation.