Effect of number of input layer units on performance of neural network systems for detection of abnormal areas from X-ray images of chest

We constructed neural network systems using one-dimensional numeric sequences from X-ray images of chest for detection of abnormal areas in the images and investigated the effect of number of input layer units on performance of the systems. In order to construct the neural networks with different number of input layer units, we changed the number of data in the input patterns, which were one-dimensional numeric sequences obtained from the two-dimensional images, by using averaging filters. Then, we produced the input patterns which consisted of 16, 32, 64 and 128 numbers of data from the one-dimensional numeric sequences. From the results, we found that the size of detectable abnormal areas in the systems was dependent on the number of input layer units in the range from 16 to 128. In addition, the performance of the systems using one-dimensional numeric sequences as the input patterns was comparable with that of the systems using two-dimensional areas. Therefore, the system used in this study is thought to be useful for the detection of abnormal areas from X-ray images of chest.