English(EN) A Comprehensive Comparison of Deep Learning Architectures for COVID-19 Classification on CT & X-ray Imagery

深度学习模型在COVID-19图像分类中达到98%的准确率

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-22 04:00

研究人员对用于从CT和X射线肺部影像中分类COVID-19的各种深度学习架构进行了综合比较。该研究使用了包括VGG、Densenet、Resnet、MobileNet、Xception、EfficientNet和NasNet在内的预训练模型。结果表明，Resnet和VGG架构在区分COVID-19阳性病例与健康肺部方面达到了95%至98%的高准确率，优于以往的文献发现。 AI

影响展示了深度学习模型在医学图像分析中的高准确性，有可能提高传染病的诊断速度和准确性。

排序理由比较特定应用深度学习模型的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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Signal score: 0 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 比较特定应用深度学习模型的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: paper, product; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: 139 days old; Aged out of breaking-news scoring windows; ranking reflects the durable signal from the full source set.

完整方法见我们的编辑标准。

报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Sarmad Khan, Arslan Shaukat, Umer Asgher, Basim Azam · 2026-05-22 04:00

用于CT和X射线影像COVID-19分类的深度学习架构综合比较

arXiv:2605.20445v1 Announce Type: cross Abstract: COVID-19 was a significant challenge that led to the loss of numerous lives daily. Not only a certain country was involved in this outbreak, but even the world has suffered because of the coronavirus. Imaging techniques using comp…

本文如何被排名

报道来源 [1]

用于CT和X射线影像COVID-19分类的深度学习架构综合比较

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