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

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 were 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 patterns obtained from the Ist 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 (0.976) was obtained with the rule-based plus ANN method. The ANNs can learn certain statistical properties associated with patterns of interstitial infiltrates in chest radiographs.