Wavelet Statistical Feature Selection Using Genetic Algorithm with Fuzzy Classifier for Breast Cancer Diagnosis

Breast cancer diagnosis at its early stage is achieved through mammogram analysis. This paper presents a genetic fuzzy system (GFS) for feature selection and mammogram classification. Mammogram image is decomposed into sub-bands using wavelet transform. Wavelet statistical features are obtained from 100 biggest wavelet coefficients from each sub-band. From each level of decomposition, 20 WSFs are extracted. Therefore, total 80 WSFs are extracted from four levels of decomposition. At first level, 20 WSFs are given to GFS, which selects five features with classification accuracy of 60.94%. For second level, 18 features are selected from 40 features and classification accuracy of 80.66% is obtained. Further, at third level, 18 features are selected from 60 features with classification accuracy of 85.25%. At last, for fourth level, 21 features are selected from 80 features and classification accuracy improved to 93.77%.

[1]  Samir Brahim Belhaouari,et al.  A statistical based feature extraction method for breast cancer diagnosis in digital mammogram using multiresolution representation , 2012, Comput. Biol. Medicine.

[2]  Prashant M Pawar,et al.  Structural Health Monitoring Of Composite Helicopter Rotor Blades , 2006 .

[3]  Ezzeddine Zagrouba,et al.  Breast cancer diagnosis in digitized mammograms using curvelet moments , 2015, Comput. Biol. Medicine.

[4]  Ranjan Ganguli,et al.  Genetic fuzzy system for damage detection in beams and helicopter rotor blades , 2003 .

[5]  Marcelo Zanchetta do Nascimento,et al.  Texture extraction: An evaluation of ridgelet, wavelet and co-occurrence based methods applied to mammograms , 2012, Expert Syst. Appl..

[6]  Banshidhar Majhi,et al.  Mammogram classification using two dimensional discrete wavelet transform and gray-level co-occurrence matrix for detection of breast cancer , 2015, Neurocomputing.

[7]  Samir Brahim Belhaouari,et al.  A comparison of wavelet and curvelet for breast cancer diagnosis in digital mammogram , 2010, Comput. Biol. Medicine.

[8]  Díbio Leandro Borges,et al.  Analysis of mammogram classification using a wavelet transform decomposition , 2003, Pattern Recognit. Lett..

[9]  Essam A. Rashed,et al.  Multiresolution mammogram analysis in multilevel decomposition , 2007, Pattern Recognit. Lett..

[10]  Sachin R. Gengaje,et al.  A novel scale and rotation invariant texture image retrieval method using fuzzy logic classifier , 2014, Comput. Electr. Eng..

[11]  A. Vadivel,et al.  A fuzzy rule-based approach for characterization of mammogram masses into BI-RADS shape categories , 2013, Comput. Biol. Medicine.

[12]  J. Dheeba,et al.  Computer-aided detection of breast cancer on mammograms: A swarm intelligence optimized wavelet neural network approach , 2014, J. Biomed. Informatics.