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Proceedings Paper

Application of artificial neural networks for reducing false positives in lung nodule detection on digital chest radiographs
Author(s): Jyh-Shyan Lin; Shih-Chung Benedict Lo; Matthew T. Freedman; Seong Ki Mun
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Paper Abstract

The objective of many existing computer-aided diagnosis (CADx) schemes for lung nodule detection is to reduce the number of false-positives (i.e., increase specificity) while maintaining a high level of sensitivity. Our examination of the false-positives obtained with the previously developed CADx program show that many round objects, such as rib crossings, end-on vessels, and aggregates of vessels, were mistakenly classified as nodules. Among the problems of decreasing the number of false-positives, the differentiation between nodules and end-on vessels is one of the most challenging tasks performed by computers. To eliminate the false-positives, two methods are proposed. One method is to extract the known features (i.e., contrast and size) based on a conventional digital image processing technique. The other method uses an artificial neural network (ANN) which is specifically trained to classify nodules and end-on vessels. Performances of the two approaches are evaluated using the receiver operating characteristics (ROC) method and the area under the ROC curve (Az). Based on our test database, the FFNN and the algorithmic approaches showed preliminary ROC performances with Az values equal to 0.90 and 0.94, respectively.

Paper Details

Date Published: 12 May 1995
PDF: 8 pages
Proc. SPIE 2434, Medical Imaging 1995: Image Processing, (12 May 1995); doi: 10.1117/12.208728
Show Author Affiliations
Jyh-Shyan Lin, Georgetown Univ. Medical Ctr. (United States)
Shih-Chung Benedict Lo, Georgetown Univ. Medical Ctr. (United States)
Matthew T. Freedman, Georgetown Univ. Medical Ctr. (United States)
Seong Ki Mun, Georgetown Univ. Medical Ctr. (United States)


Published in SPIE Proceedings Vol. 2434:
Medical Imaging 1995: Image Processing
Murray H. Loew, Editor(s)

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