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Title: Breast Lesions Classfication Using the Amalagation of Morphological and Texture Features
Authors: Bhusri, Sahil
Jain, Shruti
Virmani, Jitendra
Keywords: Breast cancer
Morphological features
Statistical features
Issue Date: 2016
Publisher: Jaypee University of Information Technology, Solan, H.P.
Abstract: The aim of this paper is to classify the breast lesions using the combination of two feature extraction techniques i.e. morphological features and the texture features. The breast lesions are characterized into two categories Benign and Malignant. Morphological Features computes Area, Perimeter, Convex area, Diameter , Major axis , Minor axis, Extent , Eccentricity, Euler no ,Solidity and Orientation where texture feature /are computed using the statistical features using FOS, GLCM, GLRL, Edge, GLDS, SFM,NGTDM, based statistical feature extraction methods. SVM classifier is extensively used for classification. Using the combination of morphological features and statistical features, the overall classification accuracy of 83.1 % is achieved and the combination of morphological and first order statistics yields the classification accuracy of 89.6%.
Description: Int J Pharm Bio Sci 2016 April; 7(2): (B) 617 - 624
ISSN: 0975-6299
Appears in Collections:Journal Articles

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