By Topic

Application of Feature Extraction and clustering in mammogram classification using Support Vector Machine

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
R. Aarthi ; Dept. of Comput. Sci. & Eng., SSN Coll. of Eng., Chennai, India ; K. Divya ; N. Komala ; S. Kavitha

Medicine is one of the major fields where the application of artificial intelligence primarily deals with construction of programs that perform diagnosis and make therapy recommendations. In digital mammography, data mining techniques are used to detect and characterize abnormalities in images and clinical reports. In the existing approaches, the mammogram image classification is done in either clinical data or statistical features of an image using neural networks and Support Vector Machine (SVM) classifier. This paper is proposed to evaluate the Application of Feature Extraction by means of combining the clinical and image features for clustering and classification in mammogram images. Initially, mammogram dataset is divided into training and test set. For the training and test sets, preprocessing techniques like noise removal and background removal are done to the images and Region of Interest (ROI) is identified. The statistical features are extracted from the ROI and the clinical data are obtained from the dataset. The feature set is clustered using k-means algorithm followed by SVM classification to classify the image as benign or malignant. The accuracy obtained from the proposed approach of clustering followed by classification is 86.11% which is higher than the direct classification approach where the accuracy is 80.0%. From the above results, the superiority of the proposed approach in terms of accuracy is justified.

Published in:

2011 Third International Conference on Advanced Computing

Date of Conference:

14-16 Dec. 2011