抄録
Pneumoconiosis analysis in chest radiographs using neural network is presented. The rounded opacities in the pneumoconiosis X-ray photo are picked up by a back propagation (BP) - neural network with several typical training patterns. The training patterns from 0.6 mm0 to 30 mm0 are made by simple circles. The neck problem for an automatic pneumoconiosis diagnosis has been to reject the unnecessary part like ribs and vessel's shades. In this paper such an unnecessary part is rejected well by adding several output neurons for own presenting neural network. These neurons are used only for picking unnecessary parts up. The input for the neural network is 30 x 30 pixel image which is quarried succeedingly from the bi-level ROI (region of interest) image with the size 500 x 500 pixel. The new technique called moving normalization is developed here in order to made an appropriate bi-level ROI image. The total evaluations is done from the size and figure categorization, many simulation examples show that the proposed method gives much reliable results than traditional ones.
