Computerized detection of mammographic lesions: Performance of artificial neural network with enhanced feature extraction

Matt Kupinski, Maryellen L. Giger, Ping Lu, Zhimin Huo

Research output: Contribution to journalConference articlepeer-review

20 Scopus citations


Artificial neural networks have been applied to the differentiation of masses from false-positive detections in digital mammograms. A database of 110 pairs of digital mammograms containing a total of 102 masses (54 malignant, 48 benign) was utilized in this study. 302 false positive regions were selected from these images to be used in the training of the artificial neural network. Over 90 features were calculated for both the true masses and the false positives. Features that showed the most separation, in a one-dimensional analysis, between true positives and false positives were selected for artificial neural network input. A three level feed-forward neural network was used with one input layer, one hidden layer and an output layer. By varying the structure and learning rate of the neural network an optimal structure was found. The performance of the ANN was evaluated by means of receiver operating characteristic (ROC) analysis and free-response receiver operating characteristic (FROC) analysis. Results from a round robin evaluation yielded an Az of 0.97 in the task of differentiating between masses and false-positive detections. In the future, multi-dimensional feature analysis will be performed to obtain the optimal performance using a combination of rule-based decision making along with artificial neural networks.

Original languageEnglish (US)
Pages (from-to)598-605
Number of pages8
JournalProceedings of SPIE - The International Society for Optical Engineering
StatePublished - May 12 1995
EventMedical Imaging 1995: Image Processing - San Diego, United States
Duration: Feb 26 1995Mar 2 1995

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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