Abstract
We construct systems including neural networks for detection of abnormal shadows in X-ray images of lungs and investigate the relationship between performance of the systems and pre-processing of input patterns. We use 32 images included 16 types of diseases and picked up 128 one-dimensional numeric sequences from the images. Then, the sequences are pre-processed for input patterns into the neural networks by averaging the values of data in the sequences and using differential filters. From the results, we found that the performance of the systems was affected by the pre-processing of the input patterns. The best values of equal error rate and AUC in our paper were 7% and 0.95, respectively, where the pixel values of the images were used as input patterns. The results obtained by using the one-dimensional numeric sequences from the images were comparable with the results of previous systems using two-dimensional information. Thus, our systems are thought to be useful for computer aided diagnosis with X-ray images of lungs.
