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Optimization of Network Topology in Computer-Aided Detection Schemes Using Phased Searching with NEAT in a Time-Scaled Framework.

Tan M, Pu J, Zheng B - Cancer Inform (2014)

Bottom Line: The result was also compared with four other well-established classifiers that include fixed-topology ANNs, support vector machines (SVMs), linear discriminant analysis (LDA), and bagged decision trees.The results show that Phased Searching outperformed the LDA and bagged decision tree classifiers, and was only significantly outperformed by SVM.From the results, the study also concluded that the three classifiers - SVM, fixed-topology ANN, and Phased Searching with NeuroEvolution of Augmenting Topologies (NEAT) in a Time-Scaled Framework - are performing comparably well in our mammographic mass detection scheme.

View Article: PubMed Central - PubMed

Affiliation: School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, USA.

ABSTRACT
In the field of computer-aided mammographic mass detection, many different features and classifiers have been tested. Frequently, the relevant features and optimal topology for the artificial neural network (ANN)-based approaches at the classification stage are unknown, and thus determined by trial-and-error experiments. In this study, we analyzed a classifier that evolves ANNs using genetic algorithms (GAs), which combines feature selection with the learning task. The classifier named "Phased Searching with NEAT in a Time-Scaled Framework" was analyzed using a dataset with 800 malignant and 800 normal tissue regions in a 10-fold cross-validation framework. The classification performance measured by the area under a receiver operating characteristic (ROC) curve was 0.856 ± 0.029. The result was also compared with four other well-established classifiers that include fixed-topology ANNs, support vector machines (SVMs), linear discriminant analysis (LDA), and bagged decision trees. The results show that Phased Searching outperformed the LDA and bagged decision tree classifiers, and was only significantly outperformed by SVM. Furthermore, the Phased Searching method required fewer features and discarded superfluous structure or topology, thus incurring a lower feature computational and training and validation time requirement. Analyses performed on the network complexities evolved by Phased Searching indicate that it can evolve optimal network topologies based on its complexification and simplification parameter selection process. From the results, the study also concluded that the three classifiers - SVM, fixed-topology ANN, and Phased Searching with NeuroEvolution of Augmenting Topologies (NEAT) in a Time-Scaled Framework - are performing comparably well in our mammographic mass detection scheme.

No MeSH data available.


ROC curves of the five compared classifiers computed over the 10-fold cross-validation experiments–(1) Phased Searching with NEAT in a Time-Scaled Framework using the maximization of AUC as the fitness function, (2) fixed-topology ANNs, (3) SVMs, (4) bagged decision trees, and (5) LDA. The error bars are symmetric, and are two standard deviation units in length.
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f4-cin-suppl.1-2014-017: ROC curves of the five compared classifiers computed over the 10-fold cross-validation experiments–(1) Phased Searching with NEAT in a Time-Scaled Framework using the maximization of AUC as the fitness function, (2) fixed-topology ANNs, (3) SVMs, (4) bagged decision trees, and (5) LDA. The error bars are symmetric, and are two standard deviation units in length.

Mentions: The computed ROC curves for all analyzed classifiers are displayed in Figure 4. We also computed and tabulated the average AUC results with standard deviation intervals over the 10 folds of each classifier in Table 2. The results indicate that SVM, fixed-topology ANNs, and Phased Searching outperform the bagged decision trees and LDA classifiers. The SVM classifier slightly outperformed the ANN and Phased Searching classifiers.


Optimization of Network Topology in Computer-Aided Detection Schemes Using Phased Searching with NEAT in a Time-Scaled Framework.

Tan M, Pu J, Zheng B - Cancer Inform (2014)

ROC curves of the five compared classifiers computed over the 10-fold cross-validation experiments–(1) Phased Searching with NEAT in a Time-Scaled Framework using the maximization of AUC as the fitness function, (2) fixed-topology ANNs, (3) SVMs, (4) bagged decision trees, and (5) LDA. The error bars are symmetric, and are two standard deviation units in length.
© Copyright Policy - open-access
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4216038&req=5

f4-cin-suppl.1-2014-017: ROC curves of the five compared classifiers computed over the 10-fold cross-validation experiments–(1) Phased Searching with NEAT in a Time-Scaled Framework using the maximization of AUC as the fitness function, (2) fixed-topology ANNs, (3) SVMs, (4) bagged decision trees, and (5) LDA. The error bars are symmetric, and are two standard deviation units in length.
Mentions: The computed ROC curves for all analyzed classifiers are displayed in Figure 4. We also computed and tabulated the average AUC results with standard deviation intervals over the 10 folds of each classifier in Table 2. The results indicate that SVM, fixed-topology ANNs, and Phased Searching outperform the bagged decision trees and LDA classifiers. The SVM classifier slightly outperformed the ANN and Phased Searching classifiers.

Bottom Line: The result was also compared with four other well-established classifiers that include fixed-topology ANNs, support vector machines (SVMs), linear discriminant analysis (LDA), and bagged decision trees.The results show that Phased Searching outperformed the LDA and bagged decision tree classifiers, and was only significantly outperformed by SVM.From the results, the study also concluded that the three classifiers - SVM, fixed-topology ANN, and Phased Searching with NeuroEvolution of Augmenting Topologies (NEAT) in a Time-Scaled Framework - are performing comparably well in our mammographic mass detection scheme.

View Article: PubMed Central - PubMed

Affiliation: School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, USA.

ABSTRACT
In the field of computer-aided mammographic mass detection, many different features and classifiers have been tested. Frequently, the relevant features and optimal topology for the artificial neural network (ANN)-based approaches at the classification stage are unknown, and thus determined by trial-and-error experiments. In this study, we analyzed a classifier that evolves ANNs using genetic algorithms (GAs), which combines feature selection with the learning task. The classifier named "Phased Searching with NEAT in a Time-Scaled Framework" was analyzed using a dataset with 800 malignant and 800 normal tissue regions in a 10-fold cross-validation framework. The classification performance measured by the area under a receiver operating characteristic (ROC) curve was 0.856 ± 0.029. The result was also compared with four other well-established classifiers that include fixed-topology ANNs, support vector machines (SVMs), linear discriminant analysis (LDA), and bagged decision trees. The results show that Phased Searching outperformed the LDA and bagged decision tree classifiers, and was only significantly outperformed by SVM. Furthermore, the Phased Searching method required fewer features and discarded superfluous structure or topology, thus incurring a lower feature computational and training and validation time requirement. Analyses performed on the network complexities evolved by Phased Searching indicate that it can evolve optimal network topologies based on its complexification and simplification parameter selection process. From the results, the study also concluded that the three classifiers - SVM, fixed-topology ANN, and Phased Searching with NeuroEvolution of Augmenting Topologies (NEAT) in a Time-Scaled Framework - are performing comparably well in our mammographic mass detection scheme.

No MeSH data available.