Performance based CBR mass detection in mammograms: applying machine learning and problem solving methods for detecting mass in digital mammogram
Valliappan Raman, Putra Sumari, J Lekha, E. George Dharma Prakash Raj · Swinburne figshare (Swinburne University of Technology) · 2010
Breast cancer continues to be a significant public health problem in the world. Early detection is the key for improving breast cancer prognosis. Mammography has been one of the most reliable methods for early detection of breast carcinomas. However, it is difficult for radiologists to provide both accurate and uniform evaluation for the enormous mammograms generated in widespread screening. The main objective of this paper is to enhance, detect and classify masses in digital mammogram. We develop a performance based casebased reasoning classification algorithm for mammographic findings to provide support for the clinical decision to perform biopsy of the breast. The developed classifier will be used for training and testing the images which is cancerous and noncancerous and improve the performance of the system.