利用基于FastViT的知识蒸,使用EfficientNet-B0进行糖尿病视网膜病变严重程度分类.
Jyotirmayee Rautaray1, Ali B M Ali2, Meenakshi Kandpal1
1Department of computer science and engineering, Odisha University of Technology and Research, Bhubaneswar, Odisha, India.
SLAS technology
|June 30, 2025
概括
本研究介绍了FastEffNet,这是一个使用知识蒸来准确检测糖尿病视网膜病变 (DR) 的深度学习框架,以降低计算成本. EfficientNet-B0实现了高精度,使得DR查更容易获得.
科学领域:
- 医学成像医学成像
- 人工智能的人工智能是人工智能.
- 计算机视觉 计算机视觉 计算机视觉
背景情况:
- 糖尿病视网膜病变 (DR) 是全球导致失明的主要原因.
- 准确和高效的自动化DR诊断对于早期干预至关重要.
- 现有的深度学习模型经常面临计算复杂性的挑战.
研究的目的:
- 开发一个新的深度学习框架,FastEffNet,用于增强糖尿病视网膜病变严重程度分类.
- 通过知识蒸来减少DR检测模型中的计算复杂性.
- 为了在诊断准确性和模型效率之间取得平衡.
主要方法:
- 使用基于变压器的知识蒸 (KD) 使用FastViT-MA26 (教师) 和EfficientNet-B0 (学生).
- 使用APTOS失明检测数据集,包括预处理,规范化,分割和增强.
- 使用准确度,精度,回忆,F1分数,卡帕分数,MCC,AUC和计算成本 (G FLOPs) 评估性能.
- 集成的Grad-CAM ++用于模型解释性,以可视化关键的视网膜区域.
主要成果:
- 作为学生模型的EfficientNet-B0实现了最高的准确性 (95.39%) 和精度 (95.43%).
- 该模型在各种指标上表现出强的表现,包括F1得分 (95.37%),CKS (0.94),WKS (0.97),MCC (0.94) 和AUC (0.99).
- 实现了0.38 G FLOP的低计算成本,突出其效率.
结论:
- 通过利用KD,FastEffNet通过轻量化架构有效地增强了DR严重性分类.
- 优化的EfficientNet-B0模型为DR查提供了一个可扩展和具有成本效益的解决方案.
- 知识蒸显示出在医学诊断中开发高效的人工智能模型的巨大潜力.
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