Multistage neural network for pattern recognition in mammogram screening

Author

Zheng, Baoyu ; Qian, Wei ; Clarke, Laurrence P.

Author_Institution

Dept. of Radiol., Univ. of South Florida, Tampa, FL, USA

Volume

6

fYear

1994

fDate

27 Jun- 2 Jul 1994

Firstpage

3437

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

Keywords

Kalman filters; backpropagation; diagnostic radiography; filtering theory; medical diagnostic computing; neural nets; pattern classification; Kalman filtering; backpropagation; breast cancer screening programs; classification; clinical analysis; digital mammography; mammogram screening; micro-calcification; multistage neural network; nonlinear decision method; pattern recognition; Backpropagation; Breast cancer; Clinical diagnosis; Filtering; Image databases; Image processing; Kalman filters; Mammography; Neural networks; Pattern recognition;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on

Conference_Location

Orlando, FL

Print_ISBN

0-7803-1901-X

Type

conf

DOI

10.1109/ICNN.1994.374887

Filename

374887