通过微调视觉变压器推进脑瘤分类:对预训练模型进行比较研究
Abdullah A Asiri1, Ahmad Shaf2, Tariq Ali2
1Radiological Sciences Department, College of Applied Medical Sciences, Najran University, Najran 61441, Saudi Arabia.
Sensors (Basel, Switzerland)
|September 28, 2023
概括
这项研究对五种视觉变压器 (ViT) 模型进行了微调,用于脑瘤图像分类. ViT-b32模型实现了98.24%的准确性,超过了现有的方法.
科学领域:
- 医学图像分析 医学图像分析
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 脑瘤分类对于诊断和治疗至关重要.
- 传统方法在准确性和效率方面面临挑战.
- 先进的深度学习模型为提高性能提供了潜力.
研究的目的:
- 评估预训练的视觉转换器 (ViT) 模型用于脑瘤图像分类的有效性.
- 使用微调方法比较五种ViT模型 (R50-ViT-l16,ViT-l16,ViT-l32,ViT-b16,ViT-b32) 的性能.
- 为了建立一个新的脑瘤分类准确性的基准.
主要方法:
- 在四个瘤类别中利用了4855个训练和857个测试图像的数据集.
- 在五个预先训练的视觉变压器 (ViT) 模型上使用了微调.
- 使用精度,回忆,F1得分,准确性和混矩阵指标评估模型性能.
主要成果:
- ViT-b32模型实现了最高的准确性,达到98.24%.
- 所有评估的ViT模型都在脑瘤分类方面表现出强的表现.
- 微调的ViT模型在分类准确性方面超过了现有的方法.
结论:
- 视觉变压器 (ViT) 模型显示出精确的大脑瘤图像分类的巨大潜力.
- ViT-b32模型为此任务提供了一个非常有效的解决方案.
- 这项研究为人工智能驱动的医学图像分析的未来进展提供了坚实的基础.
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