Classification of breast masses using a committee machine of artificial neural networks

Leandro Augusto da Silva; Emilio del Moral Hernandez; Rangaraj Mandayam Rangayyan

doi:10.1117/1.2892683

1 January 2008 Classification of breast masses using a committee machine of artificial neural networks

Leandro Augusto da Silva, Emilio del Moral Hernandez, Rangaraj Mandayam Rangayyan

Author Affiliations +

Journal of Electronic Imaging, Vol. 17, Issue 1, 013017 (January 2008). https://doi.org/10.1117/1.2892683

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 A_z under the receiver operating characteristics (ROC) curve. The A_z result for the committee machine is compared with the A_z 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 A_z 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 A_z (A_z=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.

©(2008) Society of Photo-Optical Instrumentation Engineers (SPIE)

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Leandro Augusto da Silva, Emilio del Moral Hernandez, and 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

Published: 1 January 2008

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