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Computerized detection of clustered microcalcifications in digital mammograms using a shift‐invariant artificial neural network
Author(s) -
Zhang Wei,
Doi Kunio,
Giger Maryellen L.,
Wu Yuzheng,
Nishikawa Robert M.,
Schmidt Robert A.
Publication year - 1994
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.597177
Subject(s) - artificial neural network , artificial intelligence , pattern recognition (psychology) , microcalcification , invariant (physics) , cad , computer science , region of interest , jackknife resampling , mammography , computer vision , mathematics , statistics , engineering , breast cancer , medicine , cancer , engineering drawing , estimator , mathematical physics
A computer‐aided diagnosis (CAD) scheme has been developed in our laboratory for the detection of clustered microcalcifications in digital mammograms. In this study, we apply a shift‐invariant neural network to eliminate false‐positive detections reported by the CAD scheme. The shift‐invariant neural network is a multilayer back‐propagation neural network with local, shift‐invariant interconnections. The advantage of the shift‐invariant neural network is that the result of the network is not dependent on the locations of the clustered microcalcifications in the input layer. The neural network is trained to detect each individual microcalcification in a given region of interest (ROI) reported by the CAD scheme. A ROI is classified as a positive ROI if the total number of microcalcifications detected in the ROI is greater than a certain number. The performance of the shift‐invariant neural network was evaluated by means of a jackknife (or holdout ) method and ROC analysis using a database of 168 ROIs, as reported by the CAD scheme when applied to 34 mammograms. The analysis yielded an average area under the ROC curve ( A z ) of 0.91. Approximately 55% of false‐positive ROIs were eliminated without any loss of the true‐positive ROIs. The result is considerably better than that obtained in our previous study using a conventional three‐layer, feed‐forward neural network. The effect of the network structure on the performance of the shift‐invariant neural network is also studied.