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Updated: Jul 13, 2026

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
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Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Published on: December 19, 2020

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一种混合深度神经方法,用于从肺部X射线图像中对COVID影响区域进行细分

T Vijayanandh1, A Shenbagavalli2

  • 1Department of Computer Science and Engineering, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Chennai, Tamil Nadu 600062 India.

New generation computing
|June 26, 2023
PubMed
概括

这项研究介绍了一种基于黑猩猩的Adaboost严重程度分析 (CbASA) 方法,用于从X射线图像中预测COVID-19严重程度. CbASA模型有效地消除噪音,并准确地分类疾病严重程度,优于传统方法.

关键词:
深度学习 介绍 深度学习功能提取 功能提取肺部的X射线图像 肺部的X射线图像预处理 预处理分段化 分段化 分段化 分段化严重程度分类 严重程度分类

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