Computer-aided Diagnosis for Pneumoconiosis Using Neural Network

Abstract

Computer-aided diagnosis for pneumoconiosis using Neural Network is presented. The rounded opacities on the pneumoconiosis X-ray photo are picked up quickly through a back propagation (BP) neural network with several typical training patterns. The training patterns from 0.6mmφ to 4.0mmφ are made as 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 unnecessary parts are rejected well by the special technique called "moving normalization".. The new technique called moving normalization is developed here in order to made an appropriate bi-level ROI image. The total evaluation is done from the size and figure categorization. Many simulation examples show that the proposed method gives much reliable result than traditional ones.