EVOLVING SPIKING NEURAL NETWORK TOPOLOGIES FOR BREAST CANCER CLASSIFICATION IN A DIELECTRICALLY HETEROGENEOUS BREAST

被引：2

作者：

O'Halloran, M. ^{[1
,2
]}

Cawley, S. ^{[1
,2
]}

McGinley, B. ^{[1
,2
]}

Conceicao, R. C. ^{[1
,2
]}

Morgan, F. ^{[1
,2
]}

Jones, E. ^{[1
,2
]}

Glavin, M. ^{[1
,2
]}

机构：

[1] Natl Univ Ireland Galway, Coll Engn & Informat, Univ Rd, Galway, Ireland

[2] Natl Univ Ireland Galway, NCBES, Bioelect Res Cluster, Galway, Ireland

来源：

PROGRESS IN ELECTROMAGNETICS RESEARCH LETTERS | 2011年 / 25卷

基金：

爱尔兰科学基金会;

关键词：

Diseases - Topology - Ultra-wideband (UWB) - Neural networks - Radar - Neurons;

D O I：

10.2528/PIERL11050605

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Several studies have investigated the possibility of using the Radar Target Signature (RTS) of a tumour to classify the tumour as either benign or malignant, since the RTS has been shown to be influenced by the size, shape and surface texture of tumours. The Evolved-Topology Spiking Neural Neural (SNN) presented here extends the use of evolutionary algorithms to determine an optimal number of neurons and interneuron connections, forming a robust and accurate Ultra Wideband Radar (UWB) breast cancer classifier. The classifier is examined using dielectrically realistic numerical breast models, and the performance of the classifier is compared to an existing Fixed-Topology SNN cancer classifier.

引用

页码：153 / 162

页数：10

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