In recent decades, breast cancer is the frequent cancer type in women, worldwide. The breast cancer subjects faces irreversible conditions and even death due to post treatment and diagnosis. So, automatic classification of breast cancer utilizing image techniques has great application value in the early detection of breast cancer. Due to the advance in medical field, histopathology images are regularly used in the diagnosis tool to recognize and classify the abnormality and normality cells in the images. Since, extracting non-redundant and informative features from histopathology image is a challenging task, due to heavy noise conditions, and small variant nuclei cell size. In order to highlight these issues, several models are developed by the researchers for automatic classification of breast cancer. This article investigates the existing research works performed in histopathological breast cancer classification and the problems faced by the researchers in this research area. Further, this survey article will helps the researchers to achieve significant performance in segmentation and classification of breast cancer by highlighting the problems stated in the related work section.