Breast Image Classification Based on Concatenated Statistical, Structural and Textural Features

Breast cancer is the most prevalent form of cancer. Statistics show that breast cancer causes the second highest mortality in women worldwide and around two million new cases were diagnosed every year. Accurate classification of breast cancer has acquired high importance for proper diagnosis which can save doctors and physiologist time. The breast, which contains fatty tissue is more vulnerable to cancer. In this paper, we classify a set of breast images based on Fatty tissue and non-Fatty tissue using a concatenated statistical, structural and textural feature set. For the classification, we have used Support Vector Machine (SVM) and Neural Network (NN) techniques as a classifier tool. Investigation shows that concatenated statistical, structural and textural features provide better classification result.

[1]  Kemal Polat,et al.  Breast cancer diagnosis using least square support vector machine , 2007, Digit. Signal Process..

[2]  Yinan Kong,et al.  Fingerprint image enhancement using multi-scale DDFB based diffusion filters and modified Hong filters , 2014 .

[3]  Bin Fang,et al.  Tumor cell identification using features rules , 2002, KDD.

[4]  Marti A. Hearst.  Trends & Controversies: Support Vector Machines , 1998, IEEE Intell. Syst..

[5]  Ming-Syan Chen,et al.  On Generalizable Low False-Positive Learning Using Asymmetric Support Vector Machines , 2013, IEEE Transactions on Knowledge and Data Engineering.

[6]  Muhammad Hussain,et al.  A Comparison of SVM Kernel Functions for Breast Cancer Detection , 2011, 2011 Eighth International Conference Computer Graphics, Imaging and Visualization.

[7]  S. Bacha,et al.  Appliance usage prediction using a time series based classification approach , 2012, IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society.

[8]  D. Patil,et al.  Breast cancer detection using backpropagation neural network with comparison between different neuron , 2012, 2012 2nd IEEE International Conference on Parallel, Distributed and Grid Computing.

[9]  Aruna Tiwari,et al.  A genetically optimized neural network for classification of breast cancer disease , 2014, 2014 7th International Conference on Biomedical Engineering and Informatics.

[10]  E. Fatemizadeh,et al.  Breast Cancer Diagnosis and Classification in MR-images using Multi-stage classifier , 2006, 2006 International Conference on Biomedical and Pharmaceutical Engineering.

[11]  Tariq M. Khan,et al.  Efficient hardware implementation strategy for local normalization of fingerprint images , 2016, Journal of Real-Time Image Processing.

[12]  Tore Nielsen,et al.  Revisiting the ROC curve for diagnostic applications with an unbalanced class distribution , 2013, 2013 8th International Workshop on Systems, Signal Processing and their Applications (WoSSPA).

[13]  Lilla Böröczky,et al.  Feature subset selection for improving the performance of false positive reduction in lung nodule CAD , 2006, 18th IEEE Symposium on Computer-Based Medical Systems (CBMS'05).

[14]  Yinan Kong,et al.  Side channel information analysis based on machine learning , 2014, 2014 8th International Conference on Signal Processing and Communication Systems (ICSPCS).

[15]  Hui Ding,et al.  A SVM kernel for classifying partially occluded images , 2010, 2010 International Conference on Computer Application and System Modeling (ICCASM 2010).

[16]  S. Gunn,et al.  Signal Theory for SVM Kernel Parameter Estimation , 2006, 2006 16th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing.

[17]  Yinan Kong,et al.  Side channel analysis of an elliptic curve crypto-system based on multi-class classification , 2015, 2015 6th International Conference on Computing, Communication and Networking Technologies (ICCCNT).

[18]  Alaa Eleyan.  Breast cancer classification using moments , 2012, 2012 20th Signal Processing and Communications Applications Conference (SIU).

[19]  Yinan Kong,et al.  Real-time edge detection and range finding using FPGAs , 2015 .

[20]  Vilas N. Ghate,et al.  Neural network based breast cancer classifier using electrical impedance , 2012 .

[21]  Hisao Ishibuchi,et al.  Breast Cancer Classification Using Statistical Features and Fuzzy Classification of Thermograms , 2007, 2007 IEEE International Fuzzy Systems Conference.

[22]  J. Ross Quinlan,et al.  Improved Use of Continuous Attributes in C4.5 , 1996, J. Artif. Intell. Res..

[23]  Xiangjian He,et al.  Particle swarm optimization based feature selection in mammogram mass classification , 2012, 2012 International Conference on Computerized Healthcare (ICCH).

[24]  Lior Rokach,et al.  Using the confusion matrix for improving ensemble classifiers , 2010, 2010 IEEE 26-th Convention of Electrical and Electronics Engineers in Israel.

[25]  M. Khalid,et al.  Kernel methods and support vector machines for handwriting recognition , 2002, Student Conference on Research and Development.