Cancer is the leading cause of death for all over the world. In case of cancer diagnosis the classification of cancerous tissue has an important role. This is a survey on automatic cancer detection techniques. Since the cause of the disease remains unknown, early detection and diagnosis is the key for cancer control, and it can increase the success of treatment, save lives and reduce cost. Many automated systems have been implemented to aid histopathologists and clinicians in cancer diagnosis and research, which have been attempted to significantly reduce the labor and subjectivity of traditional manual intervention with histology images. Generally, a automatic system consist of four stages: preprocessing, segmentation, feature extraction and selection, and classification. In this paper, the approaches used in these stages are summarized.