Extreme learning machine for mammographic rik analysis

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

*Awdur cyfatebol y gwaith hwn

Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion CynhadleddTrafodion Cynhadledd (Nid-Cyfnodolyn fathau)

Crynodeb

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.

Iaith wreiddiolSaesneg
Teitl2010 UK Workshop on Computational Intelligence, UKCI 2010
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsCyhoeddwyd - 09 Tach 2010
Digwyddiad2010 UK Workshop on Computational Intelligence, UKCI 2010 - Colchester, Teyrnas Unedig Prydain Fawr a Gogledd Iwerddon
Hyd: 08 Medi 201010 Medi 2010

Cyfres gyhoeddiadau

Enw2010 UK Workshop on Computational Intelligence, UKCI 2010

Cynhadledd

Cynhadledd2010 UK Workshop on Computational Intelligence, UKCI 2010
Gwlad/TiriogaethTeyrnas Unedig Prydain Fawr a Gogledd Iwerddon
DinasColchester
Cyfnod08 Medi 201010 Medi 2010

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