一个使用最佳长期短期记忆的新型分类模型,用于从CT图像中分类COVID-19

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Updated: Jul 21, 2025

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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R Vinothini1, G Niranjana2, Fitri Yakub3

  • 1Department of Computing Technologies, SRM Institute of Science and Technology, Kattankulathur, India. vr8004@srmist.edu.in.

Journal of digital imaging
|July 25, 2023
PubMed
概括

这项研究引入了一种新的深度学习模型,即基于优化算法的长期短期记忆 (POA-LSTM),用于使用CT扫描精确检测COVID-19. 该POA-LSTM方法显著提高了诊断性能,在分类冠状病毒感染方面实现了高准确性.

关键词:
计算机断层扫描扫描图像计算机断层扫描图像.新冠病毒新冠病毒新冠病毒.疾病预测 疾病预测高分辨率的网络.没有新的U-Net.佩利坎优化算法的优化算法

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