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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.


Example of a malignant mass ROI (A) and its corresponding segmentation mask (B).
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f1-cin-suppl.1-2014-017: Example of a malignant mass ROI (A) and its corresponding segmentation mask (B).

Mentions: The size of each ROI is 512 × 512 pixels, which was extracted from the center of each identified suspicious mass lesion. We used a multilayer topographic region growth algorithm9,38 to automatically segment the lesions. If there was noticeable segmentation error, the lesion boundary was corrected or re-drawn. Each lesion ROI was reduced or sub-sampled by a pixel averaging method using a kernel of 8 × 8 pixels in both x and y directions. The pixel size was thus increased from 50 × 50 μm in the original digitized image to 400 × 400 μm in the reduced image. Examples of a malignant mass and an FP detection from our dataset are displayed in Figures 1 and 2, respectively, along with their corresponding segmentations extracted by our mass segmentation scheme.


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)

Example of a malignant mass ROI (A) and its corresponding segmentation mask (B).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f1-cin-suppl.1-2014-017: Example of a malignant mass ROI (A) and its corresponding segmentation mask (B).
Mentions: The size of each ROI is 512 × 512 pixels, which was extracted from the center of each identified suspicious mass lesion. We used a multilayer topographic region growth algorithm9,38 to automatically segment the lesions. If there was noticeable segmentation error, the lesion boundary was corrected or re-drawn. Each lesion ROI was reduced or sub-sampled by a pixel averaging method using a kernel of 8 × 8 pixels in both x and y directions. The pixel size was thus increased from 50 × 50 μm in the original digitized image to 400 × 400 μm in the reduced image. Examples of a malignant mass and an FP detection from our dataset are displayed in Figures 1 and 2, respectively, along with their corresponding segmentations extracted by our mass segmentation scheme.

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.