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- W2122436060 abstract "In this paper, we investigate wavelet-based feature extraction from mammogram images and efficient dimensionality reduction techniques. The aim is to propose a new computerized feature extraction technique to identify abnormalities in breast mammogram images. In this work, dimensionality reduction is carried out using the minimal-redundancy-maximal-relevance criterion (mRMR). The classification accuracy for each set of features is measured and evaluated using machine learning techniques and support vector machines (SVMs)." @default.
- W2122436060 created "2016-06-24" @default.
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- W2122436060 date "2008-07-01" @default.
- W2122436060 modified "2023-09-23" @default.
- W2122436060 title "Comparisons of feature selection methods using discrete wavelet transforms and Support Vector Machines for mammogram images" @default.
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- W2122436060 doi "https://doi.org/10.1109/ssd.2008.4632897" @default.
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