Dependence of computer classification of clustered microcalcifications on the correct detection of microcalcifications

Our purpose was to study the dependence of computer performance in classifying clustered microcalcifications as malignant or benign on the correct detection of microcalcifications. Specifically, we studied the effects of computer-detected true-positive microcalcifications and computer-detected false-positive microcalcifications in true microcalcification clusters. Using a database of 100 mammograms, we compared computer classification performance obtained from computer-detected microcalcifications to (1) computer classification performance obtained from manually identified microcalcifications, and (2) radiologists' performance. When an artificial neural network (ANN) was trained with manually identified microcalcifications, computer classification performance was comparable to or better than radiologists' performance as the number of computer-detected true-positive microcalcifications decreased to 40% and as the number of computer-detected false-positive microcalcifications increased to 50%. Further loss in computer-detected true-positive microcalcifications degraded classification performance substantially. Moreover, training the ANN with computer-detected microcalcifications also degraded computer classification performance. These results show that computer performance in classifying clustered microcalcifications as malignant or benign is insensitive to moderate decreases in computer-detected true-positive microcalcifications and moderate increases in computer-detected false-positive microcalcifications