V. S. Pryadka, A. E. Krendal’, V. I. Kober, V. N. Karnaukhov, M. G. Mozerov
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引用次数: 0
Abstract—The main task of the study is to improve the performance of existing computer diagnostic systems using new methods for classification of benign and malignant tumors using digital mammograms. Methods and algorithms for systems of computer diagnostics are being actively developed using deep neural networks. To achieve better results on the selected data set, we transform the data using autoencoders to obtain features with low intraclass and high interclass variance. The entire working cycle of the system consists of the following stages: extraction of features using a segmented part of the pathology, division of the data into two clusters, and feature transformations using linear discriminant analysis for minimization of intraclass variance and classification of pathologies. The results of this study show that the classification of pathologies using deep learning methods makes it possible to achieve high results.
摘要--这项研究的主要任务是使用新方法提高现有计算机诊断系统的性能，以便利用数字乳房 X 光照片对良性和恶性肿瘤进行分类。目前正在利用深度神经网络积极开发计算机诊断系统的方法和算法。为了在所选数据集上取得更好的结果，我们使用自动编码器对数据进行转换，以获得类内方差小、类间方差大的特征。该系统的整个工作周期包括以下几个阶段：使用病理分割部分提取特征，将数据分为两个簇，使用线性判别分析进行特征变换，以最小化类内方差和病理分类。这项研究的结果表明，使用深度学习方法进行病理分类可以取得很好的效果。
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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