基于深度学习和可解释的人工智能的乳腺癌检测方案
Sandeep Saharan1, Niyaz Ahmad Wani2, Shreeya Chatterji2
1Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab, 147004, India. sandeepsaharan@outlook.com.
Scientific reports
|September 1, 2025
概括
通过结合卷积神经网络 (CNN) 和随机森林 (RF) 模型,DXAIB系统使用人工智能 (AI) 准确检测乳腺癌. 它通过SHAP等可解释人工智能 (XAI) 方法增强了信任,提供了清晰的诊断推理.
科学领域:
- 医学成像和诊断
- 医疗保健中的人工智能
- 机器学习用于疾病检测
背景情况:
- 人工智能在医疗保健领域具有变革潜力, 但它的"黑子"性质阻碍了信任和采用.
- 深度学习模型表现出高性能,但通常缺乏决策过程的透明度.
- 对人工智能解释性的怀疑限制了其在临床环境中的实际应用.
研究的目的:
- 推出DXAIB,一种用于精确检测乳腺癌的新型混合人工智能方案.
- 在医疗诊断中解决人工智能解释性的关键挑战.
- 增强人工智能驱动的医疗决策的透明度和信心.
主要方法:
- 一种混合方法,将卷积神经网络 (CNN) 集成为特征学习和随机森林 (RF) 进行分类.
- 实施DXAIB方案,用于自动化特征提取的卷积层.
- 利用夏普利增量解释 (SHAP) 来实现人工智能预测的本地和全球解释性.
主要成果:
- 与现有最先进的方法相比,DXAIB计划取得了更好的预测结果.
- 通过混合CNN-RF方法进行有效的乳腺癌检测.
- 提供了使用SHAP进行人工智能驱动的诊断预测的全面,特定水平的解释.
结论:
- DXAIB为准确和可解释的乳腺癌检测提供了一个有前途的解决方案.
- 整合SHAP显著提高了AI在医疗诊断中的透明度和可信度.
- DXAIB 代表了可解释性人工智能 (XAI) 在医疗应用中的重大进步.
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