应用深度转移学习来评估成像方法对结肠癌检测的影响
1Department of Computer Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
Diagnostics (Basel, Switzerland)
|May 27, 2023
概括
结合DenseNet201深度学习 (DL) 模型的结肠镜检测显示了结肠癌检测的卓越性能. 这种转移学习 (TL) 方法实现了99.1%的准确性,突出了有效的成像和DL模型组合.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 在瘤学瘤学.
背景情况:
- 准确的结肠癌检测至关重要,医学成像发挥着关键作用.
- 深度学习 (DL) 方法的性能取决于医疗图像的质量.
- 基于DL检测结肠癌的有效成像方法需要全面评估.
研究的目的:
- 用DL模型全面报告各种成像模式的性能,以检测结肠癌.
- 通过转移学习 (TL) 确定最佳的成像模式和DL模型组合用于结肠癌检测.
- 为了比较不同DL架构和组合模型的有效性.
主要方法:
- 使用了三个成像方法:计算机断层扫描,结肠镜检查和组织学.
- 采用了五种DL架构:VGG16,VGG19,ResNet152V2,MobileNetV2和DenseNet201. 这些架构包括:
- 在高性能GPU上使用5400张图像 (正常和癌症) 评估模型,包括组合模型.
主要成果:
- 使用DenseNet201模型进行结肠镜检查的最高平均性能为99.1% (AUC,精度,F1).
- 这种组合的表现优于其他所有评估的个人和集体DL模型.
- 该研究详细比较了TL环境中的成像模式和DL模型.
结论:
- 结肠镜是基于DL检测结肠癌的最有效的成像方法.
- 对于这个任务,DenseNet201是最优的DL模型,当它与结肠镜图像一起使用时.
- 这些发现指导研究组织选择有效的成像和DL策略来检测结肠癌.
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