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Title: Classification of Breast Lesions based on Laws’ Feature Extraction Techniques
Authors: Bhusri, Sahil
Jain, Shruti
Virmani, Jitendra
Keywords: Breast cancer
Primary Malignant
Secondary Malignant
Issue Date: 2016
Publisher: Jaypee University of Information Technology, Solan, H.P.
Abstract: Breast lesions are characterized into three classes which include primary benign, primary malignant and secondary malignant. In the present work Laws’ mask texture features are computed from the ultrasound images of the breast lesions. These Laws’ masks of various resolutions i.e., of length 3, 5, 7 and 9 have been used to extract the statistical features (Mean, Standard Deviation, Kurtosis, Skewness and Energy) from Laws’ texture images. In the present work using the SVM classifier, an overall classification accuracy of 88.3% and the individual classification accuracy values of 95.2% , 88.6% and 91.6% have been obtained for primary benign, primary malignant and secondary malignant classes respectively.
Description: Proceedings of the 10th INDIACom; INDIACom-2016; IEEE Conference ID: 37465 2016 3rd International Conference on “Computing for Sustainable Global Development”, 16th - 18th March, 2016 Bharati Vidyapeeth's Institute of Computer Applications and Management (BVICAM), New Delhi (INDIA)
ISSN: 0973-7529
Appears in Collections:Journal Articles

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