DocumentCode :
2910211
Title :
Extreme learning machine for mammographie risk analysis
Author :
Qu, Yanpeng ; Shen, Qiang ; Parthaláin, Neil Mac ; Wu, Wei
Author_Institution :
Dept. of Comput. Sci., Aberystwyth Univ., Aberystwyth, UK
fYear :
2010
fDate :
8-10 Sept. 2010
Firstpage :
1
Lastpage :
5
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 mammographie risk analysis.
Keywords :
mammography; neural nets; pattern classification; risk analysis; extreme learning machine; hidden layer neural network; iterative learning method; mammographic risk analysis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Intelligence (UKCI), 2010 UK Workshop on
Conference_Location :
Colchester
Print_ISBN :
978-1-4244-8774-5
Electronic_ISBN :
978-1-4244-8773-8
Type :
conf
DOI :
10.1109/UKCI.2010.5625590
Filename :
5625590
Link To Document :
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