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

  IJCTT-book-cover
 
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

MLA

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.

References
[1] Krzysztof J. Cios, G. William Moore,”Uniqueness of medical data mining”, Artificial Intelligence in Medicine 26 (2002) 1–24.
[2] Payam Homayounfar , Mieczyslaw L. Owoc,” Data Mining Research Trends in Computerized Patient Records”, Proceedings of the Federated Conference on Computer Science and Information Systems pp. 133–139 ISBN 978-83-60810-22-4.
[3] Petra Perner, ”Mining Knowledge in Medical Image Databases”, In Data Mining and Knowledge Discovery: Theory, Tools and Technology, Belur VDasarathy (eds.), Proceedings of SPIE Vol. 4057 (2000),359-369.
[4] R. Bharat Rao, Glenn Fung, Balaji Krishnapuram, Jinbo Bi, Murat Dundar, Vikas Raykar, Shipeng Yu, Sriram Krishnan, Xiang Zhou, Arun Krishnan, Marcos Salganicoff, Luca Bogoni, Matthias Wolf, Anna Jerebko, Jonathan Stoeckel, ”Mining Medical Images” Image and Knowledge Management-CAD and Knowledge Solutions (IKM-CKS) Siemens Medical Solutions USA, Inc., 51 Valley Stream Parkway, Malvern, PA-19355.
[5] Hongmei Zhu, University of Calgary,” Medical Image Processing Overview”.
[6] A.Hema1, E.Annasaro2,” A survey in need of image mining techniques”, International Journal of Advanced Research in Computer and Communication Engineering Vol. 2, Issue 2, February 2013 ISSN (Print): 2319-5940.
[7] P. Mohanaiah*, P. Sathyanarayana**, L. GuruKumar***,” Image Texture Feature Extraction Using GLCMApproach”,International Journal of Scientific and Research Publications, Volume 3, Issue 5, May 2013 1 ISSN 2250-3153.
[8] Shweta Jain,” Brain Cancer Classification Using GLCM Based Feature Extraction in Artificial Neural Network” International Journal of Computer Science & Engineering Technology (IJCSET).
[9] Fritz Albregtsen,” Statistical Texture Measures Computed from Gray Level Coocurrence Matrices” November 5, 2008.
[10] Alaa ELEYAN1, Hasan DEM?IREL2,” Co-occurrence matrix and its statistical features as a new approach for face recognition” Turk J Elec Eng & Comp Sci, Vol.19, No.1, 2011, c_T¨UB?ITAK doi:10.3906/elk-0906-27.
[11] Kun-Che Lu And Don-Lin Yang,” Image Processing and Image Mining using Decision Trees*”, Journal of information science and engineering 25, 989-1003 (2009).
[12] Angelos Tzotsos*,” A support vector machine approach for object based image analysis”, Commission IV, WG IV/4 – Commission VIII, WG VIII/11.
[13] Radu Timofte, Tinne Tuytelaars, and Luc Van Gool,” Naive Bayes Image Classification: beyond Nearest Neighbours”.
[14] D S Guru, Y. H. Sharath, S. Manjunath,” Texture Features and KNN in Classification of Flower Images”, IJCA Special Issue on “Recent Trends in Image Processing and Pattern Recognition”RTIPPR, 2010.
[15] Andreas G.K. Janecek, Wilfried N. Gansterer, Michael A. Demel, Gerhard F. Ecker,” On the Relationship Between Feature Selection and Classification Accuracy” JMLR: Workshop and Conference Proceedings 4: 90-105, New challenges for feature selection.
[16] Susana Eyheramendy, David Madigan,” A novel feature selection score for text categorization” Proceedings of the Workshop on Feature Selection for Data Mining: Interfacing Machine Learning and Statistics in conjunction with the 2005 SIAM International Conference on Data MiningApril 23, 2005.
[17] YongSeog Kim, W. Nick Street, and Filippo Menczer,” Feature Selection in Data Mining”.
[18] M. Dash 1, H. Liu2,” Feature Selection for Classification” Intelligent Data Analysis 1 (1997) 131–156.
[19] Nicolette Nicolosi,” Feature Selection Methods for Text Classification” November 7, 2008.
[20] Sanmay Das,” Filters, Wrappers and a Boosting-Based Hybrid for Feature Selection”.
[22] Abouzar Eslami and Emadodin Fatemizadeh,” Insight to Matlab Image Processing Toolbox”.
[21] Shailendra Singh, Sanjay Silakari,” An ensemble approach for feature selection of Cyber Attack Dataset” (IJCSIS) International Journal of Computer Science and Information Security, Vol. 6, No. 2, 2009.
[24] Dr. H.B.Kekre, Sudeep D. Thepade, Tanuja K. Sarode and Vashali Suryawanshi,” Image Retrieval using Texture Features extracted from GLCM, LBG and KPE” International Journal of Computer Theory and Engineering, Vol. 2, No. 5, October, 2010 1793-8201.
[26] Theodosis Goudas, Aristotle Chatziioannou,”A collaborative biomedical image-mining framework:application on the image analysis of microscopic kidney biopsies”, IEEE Journal of Biomedical and health informatics, Vol. 17,No.1, January 2013.
[27] M. K. Ghose , Ratika Pradhan, Sucheta Sushan Ghose,” Decision Tree Classification of Remotely Sensed Satellite Data using Spectral Separability Matrix”, (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 1, No.5, November 2010.
[28] Miss. Mayanka b. Khuman,” Classification of remote sensing data using k-NN method”, Journal of Information, Knowledge and Research in Electronics and Communication Engineering.
[30] Olivier Chapelle, Patrick Haffner, and Vladimir N. Vapnik,” Support Vector Machines for Histogram-Based Image Classification”, Ieee Transactions on Neural Networks, vol. 10, no. 5, september 1999.

Keywords
Image Mining, Feature Extraction, Feature Selection, SVM.