Classification of clustered microcalcifications using a Shape Cognitron neural network.
A new shape recognition-based neural network built with universal feature planes, called Shape Cognitron (S-Cognitron) is introduced to classify clustered microcalcifications. The architecture of S-Cognitron consists of two modules and an extra layer, called 3D figure layer lies in between. The first module contains a shape orientation layer, built with 20 cell planes of low level universal shape features to convert first-order shape orientations into numeric values, and a complex layer, to extract second-order shape features. The 3D figure layer is a feature extract-display layer that extracts the shape curvatures of an input pattern and displays them as a 3D figure. It is then followed by a second module made up of a feature formation layer and a probabilistic neural network-based classification layer. The system is evaluated by using Nijmegen mammogram database and experimental results show that sensitivity and specificity can reach 86.1 and 74.1%, respectively.
Lee
San Kan
SK
Department of Radiology, Taichung Veterans General Hospital, VACRS, 40705, Taichung, Taiwan, ROC.
Chung
Pau choo
Pc
Chang
Chein I
CI
Lo
Chien Shun
CS
Lee
Tain
T
Hsu
Giu Cheng
GC
Yang
Chin Wen
CW
D000465:Algorithms; D001943:Breast Neoplasms / Q000473:pathology; D002114:Calcinosis / Q000145:classification* / Q000473:pathology; D003936:Diagnosis, Computer-Assisted; D005260:Female; D006801:Humans; D008327:Mammography / Q000295:instrumentation; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated