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Updated: Jul 19, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

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优化的Xception学习模型和XgBoost分类器用于从X射线图像中检测多类胸部疾病

Kashif Shaheed1, Qaisar Abbas2, Ayyaz Hussain3

  • 1Department of Multimedia Systems, Faculty of Electronics, Telecommunication and Informatics, Gdansk University of Technology, 80-233 Gdansk, Poland.

Diagnostics (Basel, Switzerland)
|August 12, 2023
PubMed
概括

这项研究引入了一种改进的Xception (m-Xception) 模型,用于改进胸部X射线分析,准确检测COVID-19,肺炎和肺部不透明度. 人工智能模型实现了高精度,帮助放射科医生诊断各种肺部疾病.

关键词:
发现COVID-19的检测方法Xception 接收 接收 接收人工智能的人工智能是人工智能.胸部X射线 胸部X射线 胸部X射线计算机辅助检测系统 计算机辅助检测系统深度学习是一种深度学习.特性提取 特性提取开始-V3开始-V3肺炎是一种肺炎.病毒感染 病毒感染

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