A review of Recent Fuzzy-Based Methods for Computer-Aided Detection and Classification of Breast Cancer using Mammography

摘要

Breast cancer is the second most common type of cancer worldwide and one of the most common causes of cancer deaths. Worryingly, its incidence rates have kept increasing in recent years. Computer-aided detection or diagnosis (CAD) systems have been demonstrated as a key tool for helping radiologists identify cancerous signs earlier, and hence to reduce the death rate. For breast cancer diagnosis, CAD is usually based on x-ray mammography. The inherent fuzziness in the nature of mammography images makes fuzzy set theory a useful tool for handling these images. In this paper, we present an overview of recent advances in fuzzy-based methods for computer-aided detection and classification of breast cancer using mammography. Our focus is on key fuzzy logic-based methods developed for mammogram enhancement, microcalcification detection, and detection and classification of masses.