Abstract
We clarified the effect of ensemble learning on the performance of systems with neural networks, using onedimensional numeric sequences as input patterns for the detection of abnormal shadows in X-ray images of lungs. In order to implement the ensemble learning, the input patterns, which were one-dimensional numeric sequences obtained from two-dimensional images, were preprocessed using several averaging and differential filters. Then, we combined several systems with neural networks constructed using the input patterns with different preprocessing conditions. From the results, we found that the ensemble learning improved the performance of the systems with neural networks using one-dimensional numeric sequences. The best value of areas under ROC curves in the systems with ensemble learning was superior to those in previous systems with twodimensional information as input patterns. Thus, the systems proposed in this study are thought to be useful for medical diagnosis.
