American Journal of Applied Sciences

Medical Image Classification Using Genetic Optimized Elman Network

T. Baranidharan and D. K. Ghosh

DOI : 10.3844/ajassp.2012.123.126

American Journal of Applied Sciences

Volume 9, Issue 1

Pages 123-126

Abstract

Problem statement: Advancements in the internet and digital images have resulted in a huge database of images. Most of the current search engines found in the web depends only on images that can be retrieved using metadata, which generates a lot of unwanted results in the results got. Content-Based Image Retrieval (CBIR) system is the utilization of computer vision techniques in the predicament of image retrieval. In other words, it is used for searching and retrieving of the right digital image among a huge database using query image. CBIR finds extensive applications in the field of medicine as it helps medical professionals in diagnosis and plan treatment. Approach: Various methods have been proposed for CBIR using the image’s low level features like histogram, color, texture and shape. Similarly various classification algorithms like Naïve Bayes classifier, Support Vector Machine, Decision tree induction algorithms and Neural Network based classifiers have been studied extensively. In this study it is proposed to extract global features using Hilbert Transform (HT), select features based on the correlation of the extracted vectors with respect to the class label and propose a enhanced Elman Neural Network Genetic Algorithm Optimized Elman (GAOE) Neural Network. Results and Conclusion: The proposed method for feature extraction and the classification algorithm was tested on a dataset consisting of 180 medical images. The classification accuracy of 92.22% was obtained in the proposed method.

Copyright

© 2012 T. Baranidharan and D. K. Ghosh. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.