Summary: Automatic grading systems based on histopathological slide images are applied to various types of cancers. To date, cancer scientists and researchers have conducted many experiments to find and evaluate new and innovative automatic cancer grading systems to accelerate their therapeutic diagnoses and ultimately to enable more efficient prognoses. The previously proposed automatic or computer-aided systems for breast cancer grading, including specializing mitosis counting, suffer from various shortcomings. The most important one is their low efficiency along with high complexity due to the huge amount of features. In this paper, three types of features with more flexibility and less complexity are employed. These features are: completed local binary pattern (CLBP) as textural features, statistical moment entropy (SME) and stiffness matrix (SM) as a mathematical model which includes geometric, morphometric and shape-based features. In the proposed automatic mitosis detection method, these three types of features are fused with each other. The SM feature comprises of characteristics which are to be extracted for reliable discrimination of mitosis objects from non-mitosis ones. The evaluations are applied over histology datasets A and H provided by the Mitos-ICPR2012 contest sponsors. Employing both a nonlinear radial basis function (RBF) kernel for support vector machine (SVM) and also random forest classifiers, leads to the best efficiencies among the other competitive methods which have been proposed in the past. The results are in the form of F-measure criterion which is a basis for bioinformatics assessments and evaluation.