Classification of mammographic masses: comparison between Backpropagation Neural Network (BNN) and human readers

Abstract

PURPOSE: We compare mammographic mass classification performance between a backpropagation neural network (BNN), expert radiologists, and residents. Our goal is to reduce false negatives during routine reading of mammograms. METHODS: 160 cases from 3 different institutions were used. Each case contained at least one mass and had an accompanying biopsy result. Masses were extracted using region growing with seed locations identified by an expert radiologist. 10 texture and shape based features (area, perimeter, compactness, radial length, spiculation, mean/standard deviation of radial length, minimum/maximum axis, and boundary roughness) were used as inputs to a three-layer BNN. Shape features were computed on the boundary of the mass region; texture features were computed from the pixel values inside the mass. 140 cases were used for training the BNN and the remaining 20 cases were used for testing. The testing set was diagnosed by three expert radiologists, three residents, and the BNN. We evaluated the human readers and the BNN by computing the area under the ROC curve (AUC). RESULTS: The AUC was 0.923 for the BNN, 0.846 for the expert radiologists, and 0.648 for the residents. These results illustrate the promise of using BNN as a physician’s assistant for breast mass classification.