优化深度学习用于脑瘤检测:一种混合方法,具有注意力机制和临床可解释性
Aditya Jayesh Aiya1, Nishant Wani1, Mayur Ramani1
1School of Computer Science Engineering and Applications, D Y Patil International University (DYPIU), Akrudi, Pune, 411044, Maharashtra, India.
Scientific reports
|August 26, 2025
概括
一种新型的混合深度学习模型可以准确地从MRI扫描中分类脑瘤, 达到99%的准确度. 这种先进的方法提高了结质瘤,脑膜瘤和垂体瘤的诊断能力,改善了治疗计划.
科学领域:
- 医学成像
- 人工智能
- 计算生物学
背景情况:
- 从磁共振成像 (MRI) 进行脑瘤分类对于有效的治疗计划至关重要.
- 由于瘤异质性和专家解释的需要,准确的BTC具有挑战性.
- 现有的方法通常需要手动提取和标记特征,这耗时且容易出现错误.
研究的目的:
- 开发和评估一种混合深度学习 (DL) 模型,用于使用MRI进行自动脑瘤分类.
- 提高脑瘤诊断的准确性,解释性和效率.
- 将拟议模型的性能与传统机器学习方法进行比较.
主要方法:
- 一个混合DL模型集成VGG16,一个注意力机制和优化的超参数.
- 该模型使用了最先进的预处理,转移学习和梯度加权类激活映射 (Grad-CAM) 可视化.
- 使用7023张MRI图像的数据集进行培训和验证,采用端到端的学习.
主要成果:
- 拟议的混合DL模型实现了99%的测试准确性,超过了SVM,HOG,LBP和PCA等传统方法.
- 该模型显示了高精度和回忆,表明了强大的分类性能.
- 通过Grad-CAM可视化,可以了解模型的决策过程,从而提高透明度.
结论:
- 混合DL模型为MRI脑瘤分类提供了高度准确和可解释的解决方案.
- 终端学习和注意力机制自动提取特征,减少对手工标签的依赖.
- 这种方法对推进神经瘤学中的医学成像分析和临床决策具有重大潜力.
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