J. Diaz-Escobar, V. I. Kober, V. N. Karnaukhov, M. G. Mozerov
{"title":"Comparative Analysis of Convolutional Neural Networks for Classification of Breast Abnormalities","authors":"J. Diaz-Escobar, V. I. Kober, V. N. Karnaukhov, M. G. Mozerov","doi":"10.1134/s1064226923120069","DOIUrl":null,"url":null,"abstract":"<p><b>Abstract</b>—Computer-aided detection (CAD) systems are used by radiologists as a second interpreter for breast cancer detection in digital mammography. However, for every true-positive cancer detected by the CAD system, a large number of false predictions must be reviewed by an expert to avoid an unnecessary biopsy. The traditional approach to creating such systems is to select and compute the features of objects of interest from the source data, followed by the selection of a model for their classification using machine learning. Machine learning and, especially, deep learning are being used to analyze mammograms. Most of the models proposed so far are trained on a small amount of data and do not have high reliability. This paper compares several deep learning models for benign–malign mammography classification on digital mammograms. The preprocessing step is designed to remove noise and extract features using local phase information of the image. Deep learning is then used to classify the digital mammography. The experimental results are presented using several databases and estimated using several quality criteria.</p>","PeriodicalId":50229,"journal":{"name":"Journal of Communications Technology and Electronics","volume":null,"pages":null},"PeriodicalIF":0.4000,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Communications Technology and Electronics","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1134/s1064226923120069","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract—Computer-aided detection (CAD) systems are used by radiologists as a second interpreter for breast cancer detection in digital mammography. However, for every true-positive cancer detected by the CAD system, a large number of false predictions must be reviewed by an expert to avoid an unnecessary biopsy. The traditional approach to creating such systems is to select and compute the features of objects of interest from the source data, followed by the selection of a model for their classification using machine learning. Machine learning and, especially, deep learning are being used to analyze mammograms. Most of the models proposed so far are trained on a small amount of data and do not have high reliability. This paper compares several deep learning models for benign–malign mammography classification on digital mammograms. The preprocessing step is designed to remove noise and extract features using local phase information of the image. Deep learning is then used to classify the digital mammography. The experimental results are presented using several databases and estimated using several quality criteria.
摘要-计算机辅助检测（CAD）系统被放射科医生用作数字乳腺 X 射线摄影中乳腺癌检测的第二判读器。然而，计算机辅助检测系统每检测出一个真阳性癌症，就必须由专家对大量错误预测进行复查，以避免不必要的活组织检查。创建此类系统的传统方法是从源数据中选择和计算感兴趣对象的特征，然后利用机器学习为其分类选择模型。机器学习，尤其是深度学习正被用于分析乳房 X 光照片。迄今为止提出的大多数模型都是在少量数据的基础上训练出来的，可靠性不高。本文比较了几种深度学习模型，用于对数字乳腺X光照片进行良性-恶性乳腺X光照片分类。预处理步骤旨在去除噪声，并利用图像的局部相位信息提取特征。然后使用深度学习对数字乳腺 X 射线照相进行分类。实验结果使用多个数据库进行展示，并使用多个质量标准进行估算。
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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