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Advances on Tumor Image Segmentation Based on Artificial Neural Network

机译:基于人工神经网络的肿瘤图像分割研究进展

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Image technology is applied more and more to help doctors to improve the accuracy of tumor diagnosis as well as researchers to study tumor characteristics. Image segmentation technology is an important part of image treatment. This paper summarizes the advances of image segmentation by using artificial neural network including mainly the BP network and convolutional neural network (CNN). Many CNN models with different structures have been built and successfully used in segmentation of tumor images such as supervised and unsupervised learning CNN. It is shown that the application of artificial network can improve the efficiency and accuracy of segmentation of tumor image. However, some deficiencies of image segmentation by using artificial neural network still exist. For example, new methods should be found to reduce the cost of building the marked data set. New artificial networks with higher efficiency should be built.
机译:越来越多地应用图像技术来帮助医生提高肿瘤诊断的准确性以及研究肿瘤特征的研究人员。图像分割技术是图像治疗的重要组成部分。本文总结了通过使用人工神经网络的图像分割的进步,包括主要是BP网络和卷积神经网络(CNN)。许多具有不同结构的CNN模型已经建立并成功地用于肿瘤图像的分割,例如监督和无监督的学习CNN。结果表明,人工网络的应用可以提高肿瘤图像分割的效率和准确性。然而,使用人工神经网络的图像分割的一些缺陷仍然存在。例如,应发现新方法降低构建标记数据集的成本。应建造具有更高效率的新的人造网络。

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