Diagnose Breast Cancer through Mammograms Using EABCO Algorithm
Marcus Karnan · 2012
The aim of this research is the development of a reliable tool to detect early signs of breast cancer in mammographic images. Breast cancer is the most frequently diagnosed cancer and the leading cause of cancer death of female worldwide. Mammogram is one of the most excellent technologies currently being used for diagnosing breast cancer. In this paper, the Enhanced Artificial Bee Colony Optimization (EABCO) is proposed to automatically detect the breast border and nipple position to identify the suspicious regions on digital mammograms based on bilateral subtraction between left and right breast image. The algorithms are tested on digitized mammograms from MIAS database. KeywordEnhanced Artificial Bee Colony Optimization (EABCO), Microcalcifications, Mammograms,