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 language | English |
|---|---|
| Title of host publication | Proceedings of the 2010 UK Workshop on Computational Intelligence |
| Publisher | IEEE Press |
| Publication status | Published - Sept 2010 |
| Event | UKCI 2010: UK workship on Computational Intelligence - University of Essex, United Kingdom of Great Britain and Northern Ireland Duration: 08 Sept 2010 → 10 Sept 2010 |
Conference
| Conference | UKCI 2010: UK workship on Computational Intelligence |
|---|---|
| Country/Territory | United Kingdom of Great Britain and Northern Ireland |
| City | University of Essex |
| Period | 08 Sept 2010 → 10 Sept 2010 |