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A Statistical Based Feature Extraction Method for Breast Cancer Diagnosis in Digital Mammogram Using Multiresolution Representation

Brahim Belhaouari, samir and Ibrahima, faye and Mohamed, meselhy (2010) A Statistical Based Feature Extraction Method for Breast Cancer Diagnosis in Digital Mammogram Using Multiresolution Representation. Elsevier: Computer in Biology and Medicine . (Submitted)

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Abstract

This paper presents a method for breast cancer diagnosis in digital mammogram images. The wavelet is used to transform the mammogram images into a long vector of coefficients. A matrix is constructed by putting wavelet coefficients of each image in row vector, where the number of row is the number of images, and the number of columns is the number of coefficients. A feature extraction method is developed based on the statistical ttest method. The method is ranking the columns (features) according to its capability to distinguish between the different classes. The method depends on extracting the features that can maximize the ability to discriminate between different classes. Thus, the dimensionality of data features is reduced and the classification accuracy rate is improved. Then a dynamic threshold is applied to optimize the number of feature which can achieve the maximum classification accuracy rate. Support vector machine (SVM) is used to classify between the normal and abnormal and to distinguish between benign and malignant. The obtained classification accuracy rates reach to 100%.

Item Type:Article
Subjects:Q Science > Q Science (General)
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Q Science > QA Mathematics > QA76 Computer software
Departments / MOR / COE:Departments > Fundamental & Applied Sciences
ID Code:2720
Deposited By: Dr Samir Brahim Belhaouari
Deposited On:17 Mar 2011 09:06
Last Modified:19 Jan 2017 08:24

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  • A Statistical Based Feature Extraction Method for Breast Cancer Diagnosis in Digital Mammogram Using Multiresolution Representation. (deposited 17 Mar 2011 09:06) [Currently Displayed]

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