A. Yu. Makovetskii, V. I. Kober, S. M. Voronin, A. V. Voronin, V. N. Karnaukhov, M. G. Mozerov
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引用次数: 0
Abstract—An important step in solving the problem of classification and segmentation of 2D images is the extraction of local geometric features. Convolutional neural networks were widely used in recent years to solve problems in this field. Typically, the neighborhood of each pixel in an image is used to collect local geometric information. A convolutional neural network is used to extract the underlying geometric features of the neighborhood. In this work, we propose a neural network based on descriptor concatenation for two well-known neural networks to solve the problem of extracting local geometric features of mammographic images. To improve the accuracy of mammogram classification, feature filtering is used based on the calculation of joint information. Results of computer simulation are presented to illustrate the performance of the proposed method.
摘要--解决二维图像分类和分割问题的一个重要步骤是提取局部几何特征。近年来，卷积神经网络被广泛用于解决这一领域的问题。通常，图像中每个像素的邻域用于收集局部几何信息。卷积神经网络用于提取邻域的基本几何特征。在这项工作中，我们为两个著名的神经网络提出了一种基于描述符串联的神经网络，以解决提取乳房X光图像局部几何特征的问题。为了提高乳房 X 线照片分类的准确性，我们采用了基于联合信息计算的特征过滤方法。计算机模拟结果说明了所提方法的性能。
期刊介绍：
Journal of Communications Technology and Electronics is a journal that publishes articles on a broad spectrum of theoretical, fundamental, and applied issues of radio engineering, communication, and electron physics. It publishes original articles from the leading scientific and research centers. The journal covers all essential branches of electromagnetics, wave propagation theory, signal processing, transmission lines, telecommunications, physics of semiconductors, and physical processes in electron devices, as well as applications in biology, medicine, microelectronics, nanoelectronics, electron and ion emission, etc.
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