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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.


Graphs of the average network complexity (average number of connections) per generation in the run of the best-performing network (selected out of five runs), averaged on the 10 folds of Phased Searching with NEAT in a Time-Scaled Framework with alternating generations of complexification or simplification phases.
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f7-cin-suppl.1-2014-017: Graphs of the average network complexity (average number of connections) per generation in the run of the best-performing network (selected out of five runs), averaged on the 10 folds of Phased Searching with NEAT in a Time-Scaled Framework with alternating generations of complexification or simplification phases.

Mentions: Figures 5–7 display the results of the analysis performed as the change of complexification and simplification parameters selected in Phased Searching algorithm. The figures display the average and standard deviation intervals of the best fitness, complexity of the best network, and average network complexity, respectively, of the networks evolved over the 10-fold cross-validation experiments. The AUC results corresponding to the performed parameter analysis are tabulated in Table 5.


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)

Graphs of the average network complexity (average number of connections) per generation in the run of the best-performing network (selected out of five runs), averaged on the 10 folds of Phased Searching with NEAT in a Time-Scaled Framework with alternating generations of complexification or simplification phases.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4216038&req=5

f7-cin-suppl.1-2014-017: Graphs of the average network complexity (average number of connections) per generation in the run of the best-performing network (selected out of five runs), averaged on the 10 folds of Phased Searching with NEAT in a Time-Scaled Framework with alternating generations of complexification or simplification phases.
Mentions: Figures 5–7 display the results of the analysis performed as the change of complexification and simplification parameters selected in Phased Searching algorithm. The figures display the average and standard deviation intervals of the best fitness, complexity of the best network, and average network complexity, respectively, of the networks evolved over the 10-fold cross-validation experiments. The AUC results corresponding to the performed parameter analysis are tabulated in Table 5.

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.