1 January 2008 Classification of breast masses using a committee machine of artificial neural networks
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Abstract
This work proposes a new approach using a committee machine of artificial neural networks to classify masses found in mammograms as benign or malignant. Three shape factors, three edge-sharpness measures, and 14 texture measures are used for the classification of 20 regions of interest (ROIs) related to malignant tumors and 37 ROIs related to benign masses. A group of multilayer perceptrons (MLPs) is employed as a committee machine of neural network classifiers. The classification results are reached by combining the responses of the individual classifiers. Experiments involving changes in the learning algorithm of the committee machine are conducted. The classification accuracy is evaluated using the area Az under the receiver operating characteristics (ROC) curve. The Az result for the committee machine is compared with the Az results obtained using MLPs and single-layer perceptrons (SLPs), as well as a linear discriminant analysis (LDA) classifier. Tests are carried out using the student’s t-distribution. The committee machine classifier outperforms the MLP, SLP, and LDA classifiers in the following cases: with the shape measure of spiculation index, the Az values of the four methods are, in order, 0.93, 0.84, 0.75, and 0.76; and with the edge-sharpness measure of acutance, the values are 0.79, 0.70, 0.69, and 0.74. Although the features with which improvement is obtained with the committee machines are not the same as those that provided the maximal value of Az (Az=0.99 with some shape features, with or without the committee machine), they correspond to features that are not critically dependent on the accuracy of the boundaries of the masses, which is an important result.
Leandro Augusto da Silva, Emilio del Moral Hernandez, Rangaraj Mandayam Rangayyan, "Classification of breast masses using a committee machine of artificial neural networks," Journal of Electronic Imaging 17(1), 013017 (1 January 2008). https://doi.org/10.1117/1.2892683
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