通过优化预训练深度学习模型和可解释的AI,通过使用骨髓瘤瘤评估数据集来提高骨癌检测
Bolleddu Devananda Rao1, K Madhavi2
1MLR Institute of Technology, JNTUA, Hyderabad, 500043, India. dev.bolleddu@gmail.com.
Scientific reports
|November 7, 2025
概括
这项研究引入了骨癌检测优化深度学习框架 (ODLF-BCD),以提高使用组织病理图像的诊断准确性. 该框架在骨癌分类方面实现了高性能,为临床医生提供了一个透明和可靠的工具.
科学领域:
- 在瘤学瘤学.
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 从组织病理学图像准确的骨癌诊断至关重要,但当前的计算方法具有挑战性.
- 现有的深度学习方法在超参数优化,可解释性和可概括性方面存在局限性.
- 需要一个更可靠和可解释的骨癌诊断框架.
研究的目的:
- 为骨癌检测提出一个优化的深度学习框架 (ODLF-BCD).
- 提高骨癌检测的诊断准确性,透明度和通用性.
- 为临床决策支持提供一个强大的计算工具.
主要方法:
- 综合增强贝叶斯优化 (EBO) 用于超参数调整.
- 使用预先训练的模型 (EfficientNet-B4,ResNet50,DenseNet121,InceptionV3,VGG16) 进行深度转移学习.
- 整合可解释的人工智能技术 (Grad-CAM,SHAP) 用于模型可解释性和使用数据增强.
主要成果:
- EfficientNet-B4模型实现了97.9%的二进制准确度和97.3%的多类骨癌分类准确度.
- 该框架显示了高精度,回忆和F1分数.
- 可解释性方法为模型预测提供了临床解释性.
结论:
- ODLF-BCD框架对现有方法提供了显著的改进,提高了准确性和透明度.
- 拟议的系统可以作为一种可靠的替代方案,用于当前的诊断标准,如C-RAD.
- 这个框架可以作为一个决策支持系统,帮助临床医生在早期和精确的骨癌检测.
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