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使用MRI图像增强脑瘤分类的VGG-16和FTVT-b16模型的混合

Eman M Younis¹, Ibrahim A Ibrahim², Mahmoud N Mahmoud¹

¹Faculty of Computers and Information, Minia University, Minia 61519, Egypt.

Diagnostics (Basel, Switzerland)

|August 28, 2025

概括

这项研究引入了混合深度学习框架,将VGG-16和视觉变压器 (ViT) 结合起来,以从MRI扫描中准确分类脑瘤,达到99%以上的准确性.

科学领域:

医学成像
人工智能
神经瘤学

背景情况:

通过MRI准确分类脑瘤至关重要,但由于瘤的变异性和文物,
现有的方法在捕捉复杂的瘤特征方面存在局限性.

研究的目的:

开发一种用于增强脑瘤分类的新混合深度学习框架.
提高神经瘤学中人工智能辅助诊断的准确性和可靠性.

主要方法:

一个混合深度学习框架,集成VGG-16 (CNN) 和一个微调的视觉转换器 (FTVT-b16).
使用两个不同的MRI数据集进行全面评估,包括多类和二进制分类.
解决传统的CNN和纯粹的ViT的局限性.

主要成果:

在两个测试的数据集中达到高分类准确率,达到99.46%和99.90%.
证明了适合临床整合的强大和可解释的解决方案.
突出了混合架构在克服单个模型弱点方面的有效性.

结论:

拟议的混合框架显示了推进人工智能辅助脑瘤诊断的巨大潜力.
未来的研究将专注于多机构验证和临床可扩展性的计算优化.

关键词:

大脑瘤的分类卷积神经网络 (CNN)混合神经架构磁共振成像 (MRI)医学图像分析视觉变压器 (ViT)

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