Effect of dominant features on neural network performance in the classification of mammographic lesions.
Two different classifiers, an artificial neural network (ANN) and a hybrid system (one step rule-based method followed by an artificial neural network) have been investigated to merge computer-extracted features in the task of differentiating between malignant and benign masses. A database consisting of 65 cases (38 malignant and 26 benign) was used in the study. A total of four computer-extracted features--spiculation, margin sharpness and two density-related measures--was used to characterize these masses. Results from our previous study showed that the hybrid system performed better than the ANN classifier. In our current study, to understand the difference between the two classifiers, we investigated their learning and decision-making processes by studying the relationships between the input features and the outputs. A correlation study showed that the outputs from the ANN-alone method correlated strongly with one of the input features (spiculation), yielding a correlation coefficient of 0.91, whereas the correlation coefficients (absolute value) for the other features ranged from 0.19 to 0.40. This strong correlation between the ANN output and spiculation measure indicates that the learning and decision-making processes of the ANN-alone method were dominated by the spiculation measure. Three-dimensional plots of the computer output as functions of the input features demonstrate that the ANN-alone method did not learn as effectively as the hybrid system in differentiating non-spiculated malignant masses from benign masses, thus resulting in an inferior performance at the high sensitivity levels. We found that with a limited database it is detrimental for an ANN to learn the significance of other features in the presence of a dominant feature. The hybrid system, which initially applied a rule concerning the value of the spiculation measure prior to employing an ANN, prevents over-learning from the dominant feature and performed better than the ANN-alone method in merging the computer-extracted features into a correct diagnosis regarding the malignancy of the masses.
Huo
Z
Z
Department of Radiology, The University of Chicago, IL 60637, USA.
Giger
M L
ML
Metz
C E
CE
D001941:Breast Diseases / Q000145:classification / Q000000981:diagnostic imaging*; D001943:Breast Neoplasms / Q000145:classification / Q000000981:diagnostic imaging*; D016208:Databases, Factual; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008327:Mammography / Q000379:methods*; D016571:Neural Networks, Computer; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity