Extreme learning machine for mammographic rik analysis

Yanpeng Qu*, Qiang Shen, Neil Mac Parthaláin, Wei Wu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)

7 Citations (SciVal)

Abstract

The assessment of mammographie risk analysis is an important issue in the medical field. Various approaches have been applied in order to achieve a higher accuracy in such analysis. In this paper, an approach known as Extreme Learning Machines (ELM), is employed to generate a single hidden layer neural network based classifier for estimating mammographic risk. ELM is able to avoid problems such as local minima, improper learning rate, and overfitting which iterative learning methods tend to suffer from. In addition the training phase of ELM is very fast. The performance of the ELM-trained neural network is compared with a number of state of the art classifiers. The results indicate that the use of ELM entails better classification accuracy for the problem of mammographic risk analysis.

Original languageEnglish
Title of host publication2010 UK Workshop on Computational Intelligence, UKCI 2010
DOIs
Publication statusPublished - 09 Nov 2010
Event2010 UK Workshop on Computational Intelligence, UKCI 2010 - Colchester, United Kingdom of Great Britain and Northern Ireland
Duration: 08 Sept 201010 Sept 2010

Publication series

Name2010 UK Workshop on Computational Intelligence, UKCI 2010

Conference

Conference2010 UK Workshop on Computational Intelligence, UKCI 2010
Country/TerritoryUnited Kingdom of Great Britain and Northern Ireland
CityColchester
Period08 Sept 201010 Sept 2010

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