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English(EN) Energy and Performance Benchmarking of Deep Learning Models for Breast Cancer Detection

深度学习模型用于乳腺癌检测的性能和排放基准测试

作者 PulseAugur 编辑部 · [1 个来源] ·

一篇新论文对七种用于乳腺癌检测的深度学习模型进行了基准测试，评估了它们的性能和环境影响。研究发现，虽然EfficientNet和ResNet提供了高准确率，但它们也产生了更高的二氧化碳排放。DeiT-Tiny、ViT和Swin Transformer等Transformer模型取得了有竞争力的结果，其中DeiT-Tiny在一个数据集上提供了准确率和能源效率的良好平衡，而ViT和Swin在另一个数据集上表现出色。研究强调，在选择医学应用模型时，需要考虑性能、排放和数据集的具体情况。 AI

影响 强调了医学AI应用中模型性能、能耗和数据集特征之间的权衡。

排序理由 评估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 →

深度学习模型用于乳腺癌检测的性能和排放基准测试

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评估深度学习模型的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Samar Garrab, Ghada Achour ·

    用于乳腺癌检测的深度学习模型的能源和性能基准测试

    arXiv:2608.09996v1 Announce Type: cross Abstract: Recent advances in machine learning have greatly improved breast cancer detection, enabling more accurate and timely diagnosis. Deep learning (DL) models show strong potential for medical image analysis; however, as their architec…

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