Construction of high-performance systems with neural networks for detection of abnormal areas from chest X-ray images

We constructed systems with neural networks using one-dimensional numeric sequences from chest X-ray images for detection of abnormal areas and investigated the effect of pre-processing for input patterns on performance of the systems. We changed the number of data in one-dimensional numeric sequences, and applied differential filters to the sequences. Then, we produced the input patterns which consisted of pixel values, 1st-order differential coefficients and 2nd-order differential coefficients. From the results, we found that the performance of the systems using the input patterns of pixel values was superior to those using the others. The best value of Az was 0.95 when the number of units in input layer in the systems was 16. The performance of the systems in this study is thought to be comparable with that of the systems using two-dimensional areas in the images with complex image processing.