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DeepXplainer:一种可解释的基于深度学习的方法,用于使用可解释的人工智能检测肺癌.

Niyaz Ahmad Wani1, Ravinder Kumar1, Jatin Bedi1

  • 1Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala (PIN: 147004), Punjab, India.

Computer methods and programs in biomedicine
|October 28, 2023
PubMed
概括
此摘要是机器生成的。

这项研究介绍了DeepXplainer,这是一种用于肺癌检测的可解释AI模型. 它实现了高准确性,并提供了解释,增强了对人工智能驱动医疗保健的信任和临床实用性.

关键词:
人工智能的人工智能是人工智能.深度学习是一种深度学习.可解释的人工智能 (XAI)肺癌是一种肺癌.这就是 SHAP SHAP 的意思.智能医疗保健系统是一个智能系统.

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科学领域:

  • 人工智能在医学中的应用
  • 深度学习用于医学诊断
  • 可解释的人工智能 (XAI)

背景情况:

  • 人工智能 (AI) 在医疗保健领域提供了巨大的潜力,包括诊断和预测.
  • 医疗保健当前人工智能的一个主要局限性是它的"黑子"性质,阻碍了信任和采用.
  • 对于能够提供准确预测和清晰解释的可解释的人工智能模型有着至关重要的需求.

研究的目的:

  • 介绍DeepXplainer,一个新的可解释的混合深度学习技术用于肺癌检测.
  • 开发一种模型,不仅可以预测肺癌,还可以解释其预测.
  • 通过在医疗人工智能应用中提供透明度,解决对人工智能的不信任.

主要方法:

  • 开发了DeepXplainer,这是一个混合模型,结合了卷积神经网络 (CNN) 进行特征学习和XGBoost进行分类.
  • 利用SHAP (夏普利增量解释) 方法来生成模型预测的本地和全球解释.
  • 在开源"调查肺癌"数据集上训练和评估模型.

主要成果:

  • 在关键指标上,DeepXplainer与现有方法相比,实现了更高的性能.
  • 该模型显示了高精度 (97.43%),灵敏度 (98.71%) 和F1得分 (98.08%).
  • 对于每一个预测,都生成了解释,从而提高了在本地和全球层面的模型解释性.

结论:

  • 拟议的DeepXplainer是一种混合深度学习模型 (ConvXGB),有效地以高精度检测肺癌.
  • 该模型集成了特征学习,分类和预测解释组件.
  • DeepXplainer的解释性可以帮助临床医生在肺癌的检测和治疗,促进对人工智能工具更大的信任.