Diagnosis of Breast Cancer in Digital Mammograms Using Independent Component Analysis and Neural Networks

We propose a method for discrimination and classification of mammograms with benign, malignant and normal tissues using independent component analysis and neural networks. The method was tested for a mammogram set from MIAS database, and multilayer perceptron neural networks, probabilistic neural networks and radial basis function neural networks. The best performance was obtained with probabilistic neural networks, resulting in 97.3% success rate, with 100% of specificity and 96% of sensitivity.

[1]  Alessandro Bevilacqua,et al.  A novel approach to mass detection in digital mammography based on Support Vector Machines(SVM) , 2003 .

[2]  Aapo Hyvärinen,et al.  Fast and robust fixed-point algorithms for independent component analysis , 1999, IEEE Trans. Neural Networks.

[3]  Evangelos Dermatas,et al.  Neural classification of abnormal tissue in digital mammography using statistical features of the texture , 1999, ICECS'99. Proceedings of ICECS '99. 6th IEEE International Conference on Electronics, Circuits and Systems (Cat. No.99EX357).

[4]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[5]  L. Tabár,et al.  Teaching atlas of mammography. , 1983, Fortschritte auf dem Gebiete der Rontgenstrahlen und der Nuklearmedizin. Erganzungsband.

[6]  D. Kopans.  The positive predictive value of mammography. , 1992, AJR. American journal of roentgenology.

[7]  Ian W. Ricketts,et al.  The Mammographic Image Analysis Society digital mammogram database , 1994 .

[8]  Keinosuke Fukunaga,et al.  Introduction to Statistical Pattern Recognition , 1972 .

[9]  Erkki Oja,et al.  Independent Component Analysis , 2001 .

[10]  Keinosuke Fukunaga,et al.  Introduction to statistical pattern recognition (2nd ed.) , 1990 .

[11]  Erkki Oja,et al.  Independent Component Analysis for Identification of Artifacts in Magnetoencephalographic Recordings , 1997, NIPS.

[12]  Jirí Benes,et al.  On neural networks , 1990, Kybernetika.

[13]  D. Chakrabarti,et al.  A fast fixed - point algorithm for independent component analysis , 1997 .

[14]  Berkman Sahiner,et al.  Breast cancer detection: evaluation of a mass-detection algorithm for computer-aided diagnosis -- experience in 263 patients. , 2002, Radiology.

[15]  R N Vigário,et al.  Extraction of ocular artefacts from EEG using independent component analysis. , 1997, Electroencephalography and clinical neurophysiology.

[16]  G. Kokkinakis,et al.  Computer aided diagnosis of breast cancer in digitized mammograms. , 2002, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[17]  Evangelos Dermatas,et al.  Fast detection of masses in computer-aided mammography , 2000, IEEE Signal Process. Mag..

[18]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[19]  E D SHIBAEVA.  [Diagnosis of diseases of the breast]. , 1958, Vestnik rentgenologii i radiologii.

[20]  Tzyy-Ping Jung,et al.  Independent Component Analysis of Electroencephalographic Data , 1995, NIPS.

[21]  A. Hyvarinen.  A family of fixed-point algorithms for independent component analysis , 1997, 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[22]  J. Elmore,et al.  Variability in radiologists' interpretations of mammograms. , 1994, The New England journal of medicine.