Analysis of Intracranial Hemorrhage in Ct Brain Images Using Machine Learning and Deep Learning Algorithm

Abstract

Medical imaging is the process of developing the representations of all the constituents of the body visually and constituting the body physiology along with organ functions. The medical images are extracted by different techniques such as MRI (Magnetic Resonant Imaging), CT (Computer Tomography) and are processed for medical assistance and treatment. The Objective of the proposed system is to find the existence or absence of Hemorrhage in CT brain images using Supervised Machine Learning and Deep Learning Algorithm. Brain injuries may cause intracranial hemorrhages (ICH) during traumatic. This condition could leads to some disability or it may leads to death if it is not properly diagnosed. The dataset is available online at the physionet web publicly. Grayscale Conversion, Image resizing and Edge Detection Pre-processing is carried out before finding hemorrhage. The images undergo pre-processing techniques and made it suitable for further processing. This article represents intracranial hemorrhage segmentation processing and a mixture of techniques like thresholding, histogram equalization, watershed, neural network, and region based growing in CT brain images. The better hemorrhage result is based on four segmentation techniques. The advantage of using Support Vector Machine (SVM) is that, it provides results with a clear margin of separation and it is very much effective in high dimensional spaces. Convolution Neural Network is very useful to allow scenes objects and faces and for finding patterns in images. CNN remove the need for feature extraction manually by getting right from image data and using patterns or samples to classify images. Finally the result of segmentation is fed into classifier to find the Hemorrhage.