A Support Vector Machine and Information Gain based Classification Framework for Diabetic Retinopathy Images

International Journal of ComputerTrends and Technology (IJCTT)          
© 2014 by IJCTT Journal
Volume-8 Number-2                          
Year of Publication : 2014
Authors : M.Dharani , T.Menaka , G.Vinodhini
DOI :  10.14445/22312803/IJCTT-V8P112


M.Dharani , T.Menaka , G.Vinodhini . "A Support Vector Machine and Information Gain based Classification Framework for Diabetic Retinopathy Images". International Journal of Computer Trends and Technology (IJCTT) 8(2):65-69, February 2014. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract -
Image mining is the process of applying data analysis and discovery algorithms over large volume of image data. It has especially become popular in the fields of forensic sciences, fraud analysis and health care, for it reduces costs in time and money. It allows for the identification of natural group of patients given inputs such as symptoms and further classifies or predicts derivatives from the given data. It couples traditional manual medical data analysis with data mining methods for efficient computer assisted analysis. In this work, the concept of classifying the medical data with and without feature selection technique is discussed. The features representing the useful information about the images were extracted and fed to the mining process. The experimental results demonstrate that the SVM classifier can effectively and efficiently classify the data when compared to other classification algorithms. The information gain based attribute selection method provides the results similar to SVM classifier.

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Image Mining, Feature Extraction, Feature Selection, SVM.