对比多种影像技术在肺癌检测中的表现，指出3DCNN结合CT最有效
Imaging Modalities-Based Classification for Lung Cancer Detection
- 用3D卷积神经网络分析CT影像，提升检测精度
- 3D CNN在CT上表现最佳，但误报率仍高
- 适合医学影像研究者和临床医生参考
肺癌仍是全球癌症致死的首要原因。本文综述了包括先进图像处理方法在内的多种技术，重点评估其在解读CT扫描、胸部X光片及生物标志物方面的效果。研究发现，现有综述存在不足，如缺乏跨人群和多模态的强泛化模型。本研究系统整合了现有成果，旨在为研究人员和临床医生提供基础参考，推动更精准高效的肺癌检测。关键结果表明，集成3D CNN架构与CT扫描的方法表现最优，但各类模态中仍存在高误报率、数据集差异大及计算复杂度高等挑战。
原文摘要 · Abstract (English)
Lung cancer continues to be the predominant cause of cancer-related mortality globally. This review analyzes various approaches, including advanced image processing methods, focusing on their efficacy in interpreting CT scans, chest radiographs, and biological markers. Notably, we identify critical gaps in the previous surveys, including the need for robust models that can generalize across diverse populations and imaging modalities. This comprehensive synthesis aims to serve as a foundational resource for researchers and clinicians, guiding future efforts toward more accurate and efficient lung cancer detection. Key findings reveal that 3D CNN architectures integrated with CT scans achieve the most superior performances, yet challenges such as high false positives, dataset variability, and computational complexity persist across modalities.
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