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Updated: May 12, 2026

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DF-dRVFL:一种基于深度特征的新型分类器,用于乳腺质量分类

Xiang Yu1, Zeyu Ren1, David S Guttery2

  • 1School of Computing and Mathematical Sciences, University of Leicester, University Road, Leicester, LE1 7RH Leicestershire UK.

Multimedia tools and applications
|January 29, 2024
PubMed
概括

一个新的深度随机向量功能链接网络 (DF-dRVFL) 系统提高了乳腺质量分类精度. 这种计算机辅助检测可以提高乳腺癌的早期诊断,优于现有的深度学习方法.

关键词:
乳腺质量分类 乳腺质量分类深度学习是一种深度学习.在ELM中,可以选择ELM.在RVNFLNFLNFLNFLNFLNFLNFL在SNN中,SNN是SNN转移学习转移学习

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相关概念视频

Aggregates Classification01:29

Aggregates Classification

Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...