基于神经网络的X射线图像识别研究

Miaomiao Chen, Fengshan Bai
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引用次数: 0

摘要

为了使x射线图像识别有效地应用于BP神经网络。首先,对x射线图像和k均值分割图像进行预处理,提取图像特征,将提取的特征作为BP神经网络的输入,用于训练和测试网络。本文采用标准BP神经网络和改进BP神经网络对x射线图像进行识别,比较两种算法的学习率、训练误差和识别率。创新之处在于使用改进的BP网络模型来检测目标物体。它可以成功地检测出被其他物体覆盖或无遮挡的x射线图像中的物体。实验表明,改进后的BP神经网络具有学习速度快、误差小、识别率高的特点,能够有效地识别和检测x射线图像中的目标物体。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research of X Ray Image Recognition Based on Neural Network
In order to make x ray image recognition to apply to BP neural network effectively. Firstly, preprocess x ray image and k-mean segmentation image, extract image feature, The extracted feature as input of BP neural network for training and testing network. Standard BP neural network and improved BP neural network are used to recognize x ray image of this paper, then compare learning rate, training error and recognition rate of two algorithms. Innovation is using improved BP network model to detect the target object. It can successfully detect the object of x ray image with covered by other object or without occlusion. Experiment shows that improved BP neural network has faster learning rate, less error, high recognition rate, it can identify and detect the target object of x ray image effectively.
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