Abstract
Histopathology image classification can offer great support towards breast cancer identification. In this paper, an Enhanced fuzzy min max (EFMM) neural network is considered and applied to histopathological images. Gray level co-occurrence matrix (GLCM) based feature extractor is employed to extract the textural information from each histopathology image and then these information are applied on EFMM model to accurately classify the histopathology images. In this work, Breast cancer histopathology (BreaKHis) database is considered, which comprises malignant and benign histopathology images at four different magnifications. Simulation outcomes depict that the magnification factor of $100 \times$ provides greater recognition rate than other magnifying factors.
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