Using neural networks to aid the diagnosis of breast implant rupture

Linda Salchenberger, Enrique R. Venta, Luz A. Venta

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


From a database consisting of 78 implants that were surgically removed, ultrasound findings and surgical results were used to train and test backpropagation and radial basis function (RBF) neural networks using the round-robin or leave-one-out method. Receiver-operating-characteristic (ROC) curve analysis was applied to compare the performance of the different neural networks with that of the radiologists involved in the ultrasound evaluations. The neural networks outperformed the radiologists involved. RBF networks performed better in this classification problem than did backpropagation networks. The best performing network utilized, in addition to the findings, the (unaided) diagnosis of the radiologist. Thus, the 'team' approach appears to provide the best results. Also, the network performed particularly well in those cases in which the radiologist classified the implant as indeterminate. The results suggest that a neural network using findings extracted from sonograms by experienced sonographers can be of great assistance to physicians with the diagnosis of implant rupture.

Original languageEnglish (US)
Pages (from-to)435-444
Number of pages10
JournalComputers and Operations Research
Issue number5
StatePublished - May 1997

ASJC Scopus subject areas

  • Computer Science(all)
  • Modeling and Simulation
  • Management Science and Operations Research


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