The Study of Automatic Classification for Ultrasound Placenta Images Based on Adaptive Multiple Neural Networks
基于自适应多神经网络的胎盘超声图像自动分级研究

To solve the problem of automatic classification for ultrasound placenta images, we put forward an algorithm based on adaptive multiple neural networks. Two layers of BP net models were designed to carry Two-Stage separation of the placenta in this algorithm other than general one stage separation algorithm. When training networks, we do not adopt the common method which rounds the output of the networks, but propose a more reasonable grading rule, then present an adaptive threshold-gotten method to determine the reasonable placenta level. Experiments and clinic applications indicate that the similar classification result can be gotten by our algorithm as by experts, and the classification result by the algorithm before threshold division can give the doctor a good reference on the precise measurement of the placenta maturity, thereby, it has a good future in clinic applications.