Feature selection and classification of breast cancer on dynamic Magnetic Resonance Imaging by using artificial neural networks

In this paper, a new feature selection and classification methods based on artificial neural network are applied to classify breast cancer on dynamic Magnetic Resonance Imaging (MRI). The database including benign and malignant lesions is specified to select the features and classify with proposed methods. It is collected from 2004 to 2006.

[1]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[2]  Hon J. Yu,et al.  Quantitative analysis of lesion morphology and texture features for diagnostic prediction in breast MRI. , 2008, Academic radiology.

[3]  Peter Gibbs,et al.  Breast lesion analysis of shape technique: Semiautomated vs. manual morphological description , 2006, Journal of magnetic resonance imaging : JMRI.

[4]  Wee Ser,et al.  Probabilistic neural-network structure determination for pattern classification , 2000, IEEE Trans. Neural Networks Learn. Syst..

[5]  Tomoko Niwa,et al.  Using General Regression and Probabilistic Neural Networks To Predict Human Intestinal Absorption with Topological Descriptors Derived from Two-Dimensional Chemical Structures , 2003, J. Chem. Inf. Comput. Sci..

[6]  Zhimin Huo,et al.  Computerized Classification Method for Differentiating Between Benign and Malignant Lesions on Breast MR Images , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[7]  Hon J. Yu,et al.  Selection of diagnostic features on breast MRI to differentiate between malignant and benign lesions using computer-aided diagnosis: differences in lesions presenting as mass and non-mass-like enhancement , 2009, European Radiology.

[8]  R. Lucht,et al.  Neural network-based segmentation of dynamic MR mammographic images. , 2002, Magnetic resonance imaging.

[9]  P Abdolmaleki,et al.  Feature extraction and classification of breast cancer on dynamic magnetic resonance imaging using artificial neural network. , 2001, Cancer letters.

[10]  Essam Al-Daoud.  A Comparison Between Three Neural Network Models for Classification Problems , 2009 .

[11]  Maryellen L. Giger,et al.  A Fuzzy C-Means (FCM)-Based Approach for Computerized Segmentation of Breast Lesions in Dynamic Contrast-Enhanced MR Images1 , 2006 .

[12]  L. Liberman,et al.  Breast lesions detected on MR imaging: features and positive predictive value. , 2002, AJR. American journal of roentgenology.

[13]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[14]  M. Gity,et al.  Feature selection and classification of Breast Cancer on Dynamic Magnetic Resonance Imaging Using Genetic Algorithm and Artificial Neural , 2009 .

[15]  Lina Arbash Meinel,et al.  Breast MRI lesion classification: Improved performance of human readers with a backpropagation neural network computer‐aided diagnosis (CAD) system , 2007, Journal of magnetic resonance imaging : JMRI.

[16]  D. Vanel.  The American College of Radiology (ACR) Breast Imaging and Reporting Data System (BI-RADS): a step towards a universal radiological language? , 2007, European journal of radiology.

[17]  M. Giger,et al.  A fuzzy c-means (FCM)-based approach for computerized segmentation of breast lesions in dynamic contrast-enhanced MR images. , 2006, Academic radiology.