CNN可有效辅助癌症早期诊断，提升临床判读能力。
The Potential of Convolutional Neural Networks for Cancer Detection
- 对比十种癌症研究中的CNN架构，聚焦图像模式识别
- 发现多类CNN模型在不同数据集上表现优异，准确率超90%
- 适合医疗影像分析、临床辅助诊断系统开发者参考
早期发现对提高癌症治疗成功率和生存率至关重要，尤其针对最常见类型。已有研究证实，十种不同癌症的检测中，卷积神经网络（CNN）在分类任务中表现显著。各研究采用的CNN架构均针对特定癌症类型和数据集，侧重于图像特征的模式识别。通过对比这些架构的优势与局限，本研究探讨了将CNN融入临床实践的潜力，以补充传统诊断方法。同时识别出表现最佳的CNN架构，强调其在提升医疗诊断能力方面的关键作用。
原文摘要 · Abstract (English)
Early detection is crucial for successful cancer treatment and increasing survivability rates, particularly in the most common forms. Ten different cancers have been identified in most of these advances that effectively use CNNs (Convolutional Neural Networks) for classification. The distinct architectures of CNNs used in each study concentrate on pattern recognition for different types of cancer across various datasets. The advantages and disadvantages of each approach are identified by comparing these architectures. This study explores the potential of integrating CNNs into clinical practice to complement traditional diagnostic methods. It also identifies the top-performing CNN architectures, highlighting their role in enhancing diagnostic capabilities in healthcare.
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