Automatic Detection Algorithm Based on LVQ Neural Network for Micro-calcification Clusters

Given that the micro-calcification clusters in early breast cancer X-ray pictures are minimal and irregular with differentiated shapes and distributions as well as the unsatisfactorily low contrast ratio, micro-calcification clusters of small sizes and the unsatisfactorily low contrast ratio tend to be easily ignored or misdiagnosed by doctors. This paper applies the LVQ Neural Network to classify micro-calcification clusters as malignant or benign in digitized mammograms based on feature extraction of statistical methods. The result shows the method of LVQ Neural Network is simpler and effective.

[1]  M. Negnevitsky,et al.  A Neural-Fuzzy Classifier for Recognition of Power Quality Disturbances , 2002, IEEE Power Engineering Review.

[2]  M.A.A. El-Dosoky.  Computer Aided Diagnosis of Tumors Masses in Mammograms Using Texture Features , 2006, Proceedings of the Twenty Third National Radio Science Conference (NRSC'2006).

[3]  Wang Ming-yi.  Gene expression data classification based on sample filtering , 2009 .

[4]  Yan Ren,et al.  Breast cancer detection based on mixture membership function with MFSVM-FKNN ensemble classifier , 2012, 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery.

[5]  Shrinivas D. Desai,et al.  Detection of Microcalcification in Digital Mammograms by Improved-MMGW Segmentation Algorithm , 2013, 2013 International Conference on Cloud & Ubiquitous Computing & Emerging Technologies.

[6]  William R. Brody,et al.  Segmentation algorithms for detecting microcalcifications in mammograms , 1997, IEEE Transactions on Information Technology in Biomedicine.

[7]  Yi Lu,et al.  Dissimilarity based ensemble of extreme learning machine for gene expression data classification , 2014, Neurocomputing.

[8]  Jinchang Ren,et al.  Effective Classification of Microcalcification Clusters Using Improved Support Vector Machine with Optimised Decision Making , 2013, 2013 Seventh International Conference on Image and Graphics.

[9]  Tessamma Thomas,et al.  Multiplexed Wavelet Transform Technique for Detection of Microcalcification in Digitized Mammograms , 2004, Journal of Digital Imaging.

[10]  Thomas S. Huang,et al.  Spatial–Spectral Classification of Hyperspectral Images Using Discriminative Dictionary Designed by Learning Vector Quantization , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[11]  LU Jiang-jiang.  Classification of cancer gene expression data based on compressed sensing , 2012 .

[12]  Pramod K. Varshney,et al.  Noise-Enhanced Detection of Micro-Calcifications in Digital Mammograms , 2009, IEEE Journal of Selected Topics in Signal Processing.

[13]  Tadao Nakamura,et al.  Efficient parallel processing of competitive learning algorithms , 2004, Parallel Comput..

[14]  Gu Yiran,et al.  Traffic Identification Method for Specific P2P Based on Multilayer Tree Combination Classification by BP-LVQ Neural-Network , 2010, 2010 International Forum on Information Technology and Applications.