Computer-aided diagnosis of mammograms using an artificial neural network: merging of standardized input features from the ACR lexicon
Joseph Yuan-Chieh Lo, Allen T. Grisson, Carey E. Floyd, Phyllis J. Kornguth · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1995
This study aimed to develop an artificial neural network for computer-aided diagnosis in mammography, using an optimally minimized number of inputs from a standardized lexicon for mammographic features. A three-layer backpropagation neural network merged seven inputs (six radiographic findings extracted by radiologists plus age) to predict biopsy outcome as its output. Each input feature was ranked by importance, as determined by the reduction of Az when that feature was excluded and the network retrained. Once ranked, the input features were discarded in order from least to most important until performance was significantly degraded, resulting in an optimized subset of features. The neural network trained on all seven input features performed with an Az of 0.90 +/- 0.02, compared to experienced radiologists' Az of 0.88 +/- 0.02. The difference in Az was not statistically significant (p equals 0.29). The network continued to perform well given as few as three inputs: mass margin, age, and calcification description.