NDLI: CFS–SMO based classification of breast density using multiple texture models

Content Provider	Springer Nature Link
Author	Sharma, Vipul Singh, Sukhwinder
Copyright Year	2014
Abstract	It is highly acknowledged in the medical profession that density of breast tissue is a major cause for the growth of breast cancer. Increased breast density was found to be linked with an increased risk of breast cancer growth, as high density makes it difficult for radiologists to see an abnormality which leads to false negative results. Therefore, there is need for the development of highly efficient techniques for breast tissue classification based on density. This paper presents a hybrid scheme for classification of fatty and dense mammograms using correlation-based feature selection (CFS) and sequential minimal optimization (SMO). In this work, texture analysis is done on a region of interest selected from the mammogram. Various texture models have been used to quantify the texture of parenchymal patterns of breast. To reduce the dimensionality and to identify the features which differentiate between breast tissue densities, CFS is used. Finally, classification is performed using SMO. The performance is evaluated using 322 images of mini-MIAS database. Highest accuracy of 96.46 % is obtained for two-class problem (fatty and dense) using proposed approach. Performance of selected features by CFS is also evaluated by Naïve Bayes, Multilayer Perceptron, RBF Network, J48 and kNN classifier. The proposed CFS–SMO method outperforms all other classifiers giving a sensitivity of 100 %. This makes it suitable to be taken as a second opinion in classifying breast tissue density.
Starting Page	521
Ending Page	529
Page Count	9
File Format	PDF
ISSN	01400118
Journal	Medical and Biological Engineering and Computing
Volume Number	52
Issue Number	6
e-ISSN	17410444
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2014-04-26
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Breast density Texture features Mammograms Classification Feature selection Human Physiology Biomedical Engineering Imaging Radiology Computer Applications
Content Type	Text
Resource Type	Article
Subject	Biomedical Engineering Computer Science Applications

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