Multistage neural network for pattern recognition in mammogram screening

Abstract

A novel multistage neural network (MSNN) is proposed for locating and classification of micro-calcification in digital mammography. Backpropagation (BP) with Kalman filtering (KF) is used for training the MSNN. A new nonlinear decision method is proposed to improve the performance of the classification. The experimental results show that the sensitivity of this classification/detection is 100% with the false positive detection rate of less than 1 micro-calcification clusters (MCCs) per image. The proposed methods are automatic or operator independent and provide realistic image processing times as required for breast cancer screening programs. Full clinical analysis is planned using large databases.<>