An Approach for Efficient Classification of CT Scan Brain Haemorrhage Types Using GLCM Features with Multilayer Perceptron

Abstract

Feature extraction has been a difficult task in medical imaging and analysis since there remains an essential feature that may be useful for a precise identification and diagnosis purpose. Our main motive is to initiate an independent detection and classification process to improvise and accelerate the physician’s decision-making system during the emergency phase in the case of brain haemorrhages or trauma. To extract the haemorrhagic area, the other parts in and around the brain CT scan such as the skull, brain ventricles, edema tissues are to be eliminated, for which the image has to undergo processing. The process of getting the Region of Interest and the features underlies in the following steps: (a) Histogram Image intensities, (b) Otsu Thresholding, (c) Skull Removal, (d) Gray Level Co-occurrence Matrix for Feature extraction, (e) Classification using K-Nearest Neighbour and Multi Layer Perceptron algorithms for the type identification of brain haemorrhages. The identification and classification phase are used to validate the output got using the methods in both the phases. In this proposition, K-Nearest Neighbour and Multilayer Perceptron are being compared where the result obtained in classifying brain haemorrhages gave an accuracy of about 82% using K-Nearest Neighbour and 95.5% using Multilayer Perceptron.