Classification of clustered microcalcifications using a Shape Cognitron neural network

San Kan Lee, Pau Choo Chung, Chein I. Chang, Chien Shun Lo, Tain Lee, Giu Cheng Hsu, Chin Wen Yang

Research output: Contribution to journalArticlepeer-review

27 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)121-132
Number of pages12
JournalNeural Networks
Volume16
Issue number1
DOIs
Publication statusPublished - 2003 Jan

All Science Journal Classification (ASJC) codes

  • Cognitive Neuroscience
  • Artificial Intelligence

Fingerprint Dive into the research topics of 'Classification of clustered microcalcifications using a Shape Cognitron neural network'. Together they form a unique fingerprint.

Cite this