Electronic Journal of Applied Statistical Analysis

Classication of Breast Cancer Histopathological Images using Adaptive Penalized Logistic Regression with Wilcoxon Rank Sum Test

Abstract

Acharya, U. R., Raghavendra, U., Fujita, H., Hagiwara, Y., Koh, J. E., Hong, T. J.,

Sudarshan, V. K., Vijayananthan, A., Yeong, C. H., Gudigar, A., et al. (2016). Au-

tomated characterization of fatty liver disease and cirrhosis using curvelet transform

and entropy features extracted from ultrasound images. Computers in biology and

medicine, 79:250{258.

Algamal, Z. (2017a). Classication of gene expression autism data based on adap-

tive penalized logistic regression. Electronic Journal of Applied Statistical Analysis,

(2):561{571.

Algamal, Z. (2017b). An ecient gene selection method for high-dimensional microarray

data based on sparse logistic regression. Electronic Journal of Applied Statistical

Analysis, 10(1):242{256.

Algamal, Z. Y. and Lee, M. H. (2015). Penalized logistic regression with the adaptive

lasso for gene selection in high-dimensional cancer classication. Expert Systems with

Applications, 42(23):9326{9332.

Algamal, Z. Y. and Lee, M. H. (2018). A two-stage sparse logistic regression for optimal

gene selection in high-dimensional microarray data classication. Advances in Data

Analysis and Classication, pages 1{19.

Balaji, B. (2007). Rotation and scale invariant texture classication using log polar

wavelet energy signatures. PhD thesis.

Belsare, A., Mushrif, M., Pangarkar, M., and Meshram, N. (2015). Classication of

breast cancer histopathology images using texture feature analysis. In TENCON

-2015 IEEE Region 10 Conference, pages 1{5. IEEE.

Chen, Y., Wang, L., Li, L., Zhang, H., and Yuan, Z. (2016). Informative gene selection

and the direct classication of tumors based on relative simplicity. BMC bioinformat-

ics, 17(1):44.

Fan, J. and Li, R. (2001). Variable selection via nonconcave penalized likelihood and its

oracle properties. Journal of the American statistical Association, 96(456):1348{1360.

Feng, Z. Z., Yang, X., Subedi, S., and McNicholas, P. D. (2012). The lasso and sparse least squares regression methods for snp selection in predicting quantitative traits.

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB),

(2):629{636.

Friedman, J., Hastie, T., and Tibshirani, R. (2010). Regularization paths for generalized

linear models via coordinate descent. Journal of statistical software, 33(1):1.

Gonzalez, R. C. (2008). Rew digital image processing.

Hoerl, A. E. and Kennard, R. W. (1970). Ridge regression: Biased estimation for

nonorthogonal problems. Technometrics, 12(1):55{67.

Kahya, M. A. (2019). Classication enhancement of breast cancer histopathological

image using penalized logistic regression. Indonesian Journal of Electrical Engineering

and Computer Science, 13(1):405{410.

Kahya, M. A., Al-Hayani, W., and Algamal, Z. Y. (2017). Classication of breast cancer

histopathology images based on adaptive sparse support vector machine. Journal of

Applied Mathematics and Bioinformatics, 7(1):49.

Korkmaz, S., Zararsiz, G., and Goksuluk, D. (2014). Drug/nondrug classication using

support vector machines with various feature selection strategies. Computer methods

and programs in biomedicine, 117(2):51{60.

Liao, C., Li, S., and Luo, Z. (2006). Gene selection using wilcoxon rank sum test

and support vector machine for cancer classication. In International Conference on

Computational and Information Science, pages 57{66. Springer.

Liu, Z., Jiang, F., Tian, G.,Wang, S., Sato, F., Meltzer, S. J., and Tan, M. (2007). Sparse

logistic regression with lp penalty for biomarker identication. Statistical Applications

in Genetics and Molecular Biology, 6(1).

Mao, Z., Cai, W., and Shao, X. (2013). Selecting signicant genes by randomization test

for cancer classication using gene expression data. Journal of biomedical informatics,

(4):594{601.

Misiti, M. (2000). Wavelet Toolbox for Use with MATLAB: User's Guide; Version 2;

Computation, Visualization, Programming. MathWorks Incorporated.

Park, H., Shiraishi, Y., Imoto, S., and Miyano, S. (2016). A novel adaptive penalized lo-

gistic regression for uncovering biomarker associated with anti-cancer drug sensitivity.

IEEE/ACM transactions on computational biology and bioinformatics, 14(4):771{782.

Park, M. Y. and Hastie, T. (2007). Penalized logistic regression for detecting gene

interactions. Biostatistics, 9(1):30{50.

Qasim, O. S. and Algamal, Z. Y. (2018). Feature selection using particle swarm

optimization-based logistic regression model. Chemometrics and Intelligent Laboratory

Systems, 182:41{46.

Singh, B. K., Verma, K., and Thoke, A. (2016). Fuzzy cluster based neural network

classier for classifying breast tumors in ultrasound images. Expert Systems with

Applications, 66:114{123.

Spanhol, F. A., Oliveira, L. S., Petitjean, C., and Heutte, L. (2015). A dataset for

breast cancer histopathological image classication. IEEE Transactions on Biomedical Engineering, 63(7):1455{1462.

Sudarshan, V. K., Mookiah, M. R. K., Acharya, U. R., Chandran, V., Molinari, F.,

Fujita, H., and Ng, K. H. (2016). Application of wavelet techniques for cancer diagnosis

using ultrasound images: A review. Computers in biology and medicine, 69:97{111.

Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the

Royal Statistical Society: Series B (Methodological), 58(1):267{288.

Vu, T. H., Mousavi, H. S., Monga, V., Rao, G., and Rao, U. A. (2015). Histopathological

image classication using discriminative feature-oriented dictionary learning. IEEE

transactions on medical imaging, 35(3):738{751.

Zhang, L., Qian, L., Ding, C., Zhou, W., and Li, F. (2015). Similarity-balanced discrim-

inant neighbor embedding and its application to cancer classication based on gene

expression data. Computers in biology and medicine, 64:236{245.

Zhang, Y., Zhang, B., and Lu, W. (2013). Breast cancer histological image classication

with multiple features and random subspace classier ensemble. In Knowledge-Based

Systems in Biomedicine and Computational Life Science, pages 27{42. Springer.

Zou, H. (2006). The adaptive lasso and its oracle properties. Journal of the American

statistical association, 101(476):1418{1429.

Zou, H. and Hastie, T. (2005). Regularization and variable selection via the elastic net.

Journal of the royal statistical society: series B (statistical methodology), 67(2):301{