Brain Tumor Segmentation with Deep Learning Technique

The proposed work is based on Deep learning technique which is a deep neural network and probabilistic neural network to detect unwanted masses in the brain. Our work is personalized for both high and low-level grades. Tumors can appear in anyplace of the brain and its natures like shape, contrast, and size have always been an uncertain one, which means that there is no standard fact about tumor structure. The rate at which people woe from brain tumor becomes increasing nowadays. These reasons stimulate us to provide an intelligent solution which uses deep learning technique to segment abnormal tissues in the brain. It can help to find out whether the tumor is in the brain or not. With the help of these MRI images, segmentation can be performed and the segmented images can be compared with the normal brain tissues also with the tumor cells. The results are provided (whether the brain contains a tumor or not) based on the comparison. In this paper, the segmentation is done using a convolution neural network and Probabilistic neural network. Here, the comparison sketch of various models is done. Based on that, we discovered an architecture which is based on Convolutional Neural Networks (CNN) with both 3*3 and 7*7 in an overlapped manner, and build a cascaded architecture, so that we can able to segment a tumor accurately in an effective manner, since we use Image dataset Brats13. Similarly, we use a probabilistic neural network for detecting tumors and compare the result of both of them. We proposed a unique CNN and PNN architectures which are different from those conventional models used in image processing and computer vision techniques. Our model deals with both local and global features.