Extreme Learning Machine for Mammographic Risk Analysis

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Abstract

The assessment of mammographic 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 publicationProceedings of the 2010 UK Workshop on Computational Intelligence
PublisherIEEE Press
Publication statusPublished - Sept 2010
EventUKCI 2010: UK workship on Computational Intelligence - University of Essex, United Kingdom of Great Britain and Northern Ireland
Duration: 08 Sept 201010 Sept 2010

Conference

ConferenceUKCI 2010: UK workship on Computational Intelligence
Country/TerritoryUnited Kingdom of Great Britain and Northern Ireland
CityUniversity of Essex
Period08 Sept 201010 Sept 2010

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