Investigation of the effect of Training Data on the Performance of Support Vector Machine in Classification of BrainMR Images

Abstract

In recent years, a wide research is being carried out on brain imaging which involves computer aided detection of abnormalities in brain. Out of many diagnostic imaging techniques for the early detection of any abnormal changes in brain tissues, Magnetic Resonance Imaging (MRI) is a widely-used imaging method. The shortage of radiologists for analyzing the brain MR images calls for an automated system to analyze and classify such medical images. Support Vector Machine (SVM) has been widely used in the recent years to classify brain MR images into different classes. SVM Classifiers perform the task of classification in two phases – training phase and testing phase. The amount of image data to be used for training plays a vital role in determining the accuracy of the SVM. This paper focuses on determining the optimal number of image data in the training set for which a better classification accuracy is obtained. Classification experiments with various percentages of data in the training set show that 80% of total dataset is the optimal one. Results also point out that Polynomial kernel function of SVM is more apt for brain MR images classification with classification accuracy of 100% when trained with 80% of data

Authors and Affiliations

Swetha K. T

Keywords

Related Articles

Studies of Fresh Water Toxic Phytoplanktonic (Microcystis) of Keenkjhar Lake, Thatta, Sindh, and Pakstan

The present study is the first study of Keenjhar lake carried out to gain the knowledge about the toxic Phytoplanktonic Cyanophyta (Microcystis). In which total of 14 phytoplanktonic Cyanophyta (Microcystis) species were...

Detection of Spine Diseases by Feature Change in Aging Lumbar Spine Using Factor Analysis

The vertebrae’s and intervertebral discs form the axis of the skeleton which is said to be spine. The spine consists of 33 vertebrae’s which are grouped as five regions named as cervical region, thoracic region, lumbar r...

ARC GIS BASED STUDY OF WATER QUALITY OF PHULELI CANAL HYDERABAD, SINDH PAKISTAN

Phulleli canal takes off from river Indus and passes through Hyderabad city, where most of untreated sewage of Hyderabad city is added. Five pumping stations lifting the water from Phulleli canal for agricultural purpose...

Brand New Query Processing Using Seed Block Algorithm on Cloud Storage

Now-a-days incrementing users of public cloud computing infrastructures, utilizing clouds to store data with query accommodations are solution which gives more scalability and cost-preserving. Hence, most of the data is...

A Saliency Detection Model Based on Wavelet Transform Through Fusion of Color Spaces

Visual attention is studied by detecting a salient object in an input image. Visual attention is used in various image processing applications such as image segmentation, patch rarities, pattern recognition etc. In this...

Download PDF file
  • EP ID EP220461
  • DOI -
  • Views 156
  • Downloads 0

How To Cite

Swetha K. T (2014). Investigation of the effect of Training Data on the Performance of Support Vector Machine in Classification of BrainMR Images. International journal of Emerging Trends in Science and Technology, 1(4), 417-421. https://europub.co.uk/articles/-A-220461