Application of artificial neural networks for reducing false positives in lung nodule detection on digital chest radiiographs

Jyh-Shyan Lin; Shih-Chung Benedict Lo; Matthew T. Freedman M.D.; Seong Ki Mun

doi:10.1117/12.208728

12 May 1995 Application of artificial neural networks for reducing false positives in lung nodule detection on digital chest radiographs

Jyh-Shyan Lin, Shih-Chung Benedict Lo, Matthew T. Freedman M.D., Seong Ki Mun

Author Affiliations +

Proceedings Volume 2434, Medical Imaging 1995: Image Processing; (1995) https://doi.org/10.1117/12.208728
Event: Medical Imaging 1995, 1995, San Diego, CA, United States

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 (A_z). Based on our test database, the FFNN and the algorithmic approaches showed preliminary ROC performances with A_z values equal to 0.90 and 0.94, respectively.

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Jyh-Shyan Lin, Shih-Chung Benedict Lo, Matthew T. Freedman M.D., and Seong Ki Mun "Application of artificial neural networks for reducing false positives in lung nodule detection on digital chest radiographs", Proc. SPIE 2434, Medical Imaging 1995: Image Processing, (12 May 1995); https://doi.org/10.1117/12.208728

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