  In this paper we present a novel method for an automated diagnosis of breast
carcinoma through multilevel iterative variational mode decomposition (VMD) and
textural features encompassing Zernaike moments, fractal dimension and entropy
features namely, Kapoor entropy, Renyi entropy, Yager entropy features are
extracted from VMD components. The proposed method considers the
histopathological image as a set of multidimensional spatially-evolving
signals. ReliefF algorithm is used to select the discriminatory features and
statistically most significant features are fed to squares support vector
machine (SVM) for classification. We evaluate the efficiency of the proposed
methodology on publicly available Breakhis dataset containing 7,909 breast
cancer histological images, collected from 82 patients, of both benign and
malignant cases. Experimental results shows the efficacy of the proposed method
in outperforming the state of the art while achieving an average classification
rates of 89.61% and 88:23% using three-fold and ten-fold cross-validation
strategies, respectively. This system can aid the pathologist in accurate and
reliable diagnosis of biopsy samples. BreaKHis, a publicly dataset available at
http://web.inf.ufpr.br/vri/breast-cancer-database.
