Abnormality Detection in Brain CT Image Using Support Vector Machine

7 pages•Published: October 23, 2018

Abstract

Automated detection of the abnormalities in brain image analysis is very important and it is prerequisite for planning and treatment of the disease. Computed tomography scan is an imaging technique used for studying brain images. Classification of brain images is important in order to distinguish between normal brain images and those having the abnormalities in brain like hematomas, tumor, edema, concussion etc. The proposed automated method identifies the abnormalities in brain CT images and classifies them using support vector machine. The proposed method consists of three important phases, First phase is preprocessing, second phase consists of feature extraction and final phase is classification. In the first phase preprocessing is performed on brain CT images to remove artifacts and noise. In second phase features are extracted from brain CT images using gray level co-occurrence matrix (GLCM). In the final stage, extracted features are fed as input to SVM classifier with different kernel functions that classifies the images into normal and abnormal with different accuracy levels.

Keyphrases: Computed tomography scan, Hematomas, Support Vector Machine (SVM)

In: Vinay K Chandna, Vijay Singh Rathore and Shikha Maheshwari (editors). Proceedings on International Conference on Emerging Trends in Expert Applications & Security (2018), vol 2, pages 74--80

Links:	https://easychair.org/publications/paper/S4QF
	https://doi.org/10.29007/jsfg