FLPneXAINet:联合深度学习和可解释的人工智能用于改进肺炎预测,利用GAN增强的胸部X射线数据
Shuvo Biswas1, Rafid Mostafiz2, Mohammad Shorif Uddin3
1Department of Information and Communication Technology, Mawlana Bhashani Science and Technology University, Tangail, Bangladesh.
PloS one
|July 17, 2025
概括
这项研究介绍了FLPneXAINet,一个使用联合学习 (FL) 和可解释AI (XAI) 的框架,用于通过胸部X射线准确检测肺炎. 它实现了高性能,同时保护了患者的隐私.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算生物学 计算生物学
背景情况:
- 由于症状重叠和与临床数据共享的隐私问题,肺炎诊断具有挑战性.
- 通过胸部X射线 (CXR) 准确及时检测肺炎对于有效治疗至关重要.
- 现有的方法难以平衡诊断准确性与患者数据隐私.
研究的目的:
- 开发和评估FLPneXAINet,这是一个整合联合学习 (FL),深度学习 (DL) 和可解释AI (XAI) 的新型框架.
- 通过使用CXR图像实现安全和准确的肺炎预测,同时保持患者的隐私.
- 通过可解释的人工智能见解,增强医疗保健专业人员的诊断能力.
主要方法:
- 使用Kaggle CXR数据集 (8,402张图像) 进行预处理并使用CycleGAN进行增强.
- 使用预先训练的DL模型 (VGG16,NASNetMobile,MobileNet) 和集体DL (EDL) 来进行特征提取.
- 在联合学习环境中使用ML模型 (KNN,NB,SVM,RF) 进行特征优化 (RFE,ANOVA,RF) 和预测.
主要成果:
- 在FLPneXAINet框架内的集体深度学习 (EDL) 模型实现了卓越的性能.
- 在肺炎预测方面实现了高精度 (97.61%),F1得分 (98.36%),回忆 (98.13%) 和精度 (98.59%).
- 可解释的人工智能技术 (LIME,Grad-CAM) 验证了框架的预测,提高了可解释性.
结论:
- FLPneXAINet提供了一个强大的,保护隐私的解决方案,用于从CXR图像中准确诊断肺炎.
- 整合FL,DL和XAI显著提高了诊断准确性和临床实用性.
- 该框架支持医疗保健专业人员做出及时和明智的肺炎治疗决策.
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