先进的动态组合框架,具有可解释性驱动的洞察力,用于跨数据集的精确脑瘤分类
Retinderdeep Singh1, Sheifali Gupta1, Ashraf Osman Ibrahim2,3
1Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India.
Scientific reports
|August 8, 2025
概括
这项研究引入了一种先进的深度学习系统,用于从MRI扫描中精确地分类脑瘤. 它达到99.4%的准确性,提高了医疗成像中的诊断可靠性和解释性.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 由于瘤的多样性和人为错误,精确的脑瘤检测具有挑战性.
- 现有的诊断方法需要提高精度和可靠性.
研究的目的:
- 开发一种新的集体深度学习系统,使用MRI数据准确地分类脑瘤.
- 为了提高诊断准确度,并在脑瘤检测中提供解释性.
主要方法:
- 一个整体框架,整合了微调的卷积神经网络 (CNN),ResNet-50和EfficientNet-B5.
- 适应性动态重量分布和定制加权交叉损失函数.
- 可解释的AI (XAI) 技术 (Grad-CAM,SHAP,SmoothGrad,LIME) 和基于的不确定性分析.
主要成果:
- 实现了高分类准确率:99.4% (测试),99.48% (验证) 和99.31% (交叉数据集).
- 通过XAI技术证明了强大的解释性.
- 量化预测信心与0.3093的平均,识别不确定的预测.
结论:
- 拟议的深度学习框架为脑瘤分类提供了高精度,稳定性和可解释性.
- 它显示了集成到自动诊断系统的巨大潜力,减少了错误.
- 该系统有效地解决了阶级不平衡,并改善了模型通用化.
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