Fully Automated Magnetic Resonance Detection and Segmentation of Brain using Convolutional Neural Network

Abstract

     The brain's magnetic resonance imaging (MRI) is tasked with finding the pixels or voxels that establish where the brain is in a medical image The Convolutional Neural Network (CNN) can process curved baselines that frequently occur in scanned documents. Next, the lines are separated into characters. In the Convolutional Neural Network (CNN) can process curved baselines that frequently occur in scanned documents case of fonts with a fixed MRI width, the gaps are analyzed and split. Otherwise, a limited region above the baseline is analyzed, separated, and classified. The words with the lowest recognition score are split into further characters x until the result improves. If this does not improve the recognition score, contours are merged and classified again to check the change in the recognition score. The features for classification are extracted from small fixed-size patches over neighboring contours and matched against the trained deep learning representations this approach enables Tesseract to easily handle MRI sample results broken into multiple parts, which is impossible if each contour is processed separately Hard to read! Try to split sentences. The CNN inception network seem to be a suitable choice for the evaluation of the synthetic MRI samples with 3000 features, and 12000 samples of images as data augmentation capacities favors data which is similar to the original training set and thus unlikely to contain new information content with an accuracy of 98.68%. The error is only 1.32% with the increasing the number of training samples, but the most significant impact in reducing the error can be made by increasing the number of samples.