Artificial neural networks in chest radiographs: detection and characterization of interstitial lung disease

Takayuki Ishida; Shigehiko Katsuragawa; Kazuto Ashizawa; Heber MacMahon; Kunio Doi

doi:10.1117/12.274182

25 April 1997 Artificial neural networks in chest radiographs: detection and characterization of interstitial lung disease

Takayuki Ishida, Shigehiko Katsuragawa, Kazuto Ashizawa, Heber MacMahon, Kunio Doi

Author Affiliations +

Proceedings Volume 3034, Medical Imaging 1997: Image Processing; (1997) https://doi.org/10.1117/12.274182
Event: Medical Imaging 1997, 1997, Newport Beach, CA, United States

Abstract

We have developed a computerized scheme for detection of interstitial lung disease by using artificial neural networks (ANNs) on quantitative analysis of digital image data. Three separate ANNs wee applied for the ANN scheme. The first ANN was trained with horizontal profiles in the ROIs selected from digital chest radiographs. The second ANN was trained with vertical output pattern obtained from the 1st ANN in each ROI. The output from the 2nd ANN was used to distinguish between normal and abnormal ROIs. In order to improve the performance, we attempted a density correction and rib edge removal. The A_z value was improved from 0.906 to 0.934 by incorporating density correction. For the classification of each chest image, we employed a rule-based method and a rule-based plus the third ANN method. A high A_z value was obtained with the rule-based plus ANN method. The ANNs can learn certain statistical properties associate with patterns of interstitial infiltrates in chest radiographs.

Citation Download Citation

Takayuki Ishida, Shigehiko Katsuragawa, Kazuto Ashizawa, Heber MacMahon, and Kunio Doi "Artificial neural networks in chest radiographs: detection and characterization of interstitial lung disease", Proc. SPIE 3034, Medical Imaging 1997: Image Processing, (25 April 1997); https://doi.org/10.1117/12.274182

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