Effect of pre-processing on performance of a neural network with one-dimensional sampling from X-ray images of chest

To judge whether the images from X-ray of chest are abnormal or not, we constructed the system including a three-layered neural network with one-dimensional sampling and investigated the effect of pre-processing, FFT (Fast Fourier Transform) on the performance of the system for medical diagnosis support. We used one-dimensional lines from two-dimensional images from X-ray of chest as input patterns for the neural network. From the results, we found that square error of our neural network decreased for learning patterns, and that there was broden region, where the FAR(False Acceptance Rate) and FRR(False Rejection Rate) were zero. This indicated that our system with the combination of FFT and the one-dimensional sampling was useful for judgement whether the images from X-ray of chest were abnormal or not. In addition, we found that the network with the less learning iterations for convergence did not miss for any abnormal test patterns with low FRR for normal test patterns. Thus, we suggested a possibility of useful medical diagnosis support system by using the neural network.