AlterNet-K:用于检测青光眼的小型和紧型号
Gavin D'Souza1, P C Siddalingaswamy2, Mayur Anand Pandya2
1Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka 576104 India.
Biomedical engineering letters
|January 8, 2024
概括
早期发现青光眼对于预防失明至关重要. 结合ResNets和多头自我注意力 (MSA) 的新AlterNet-K模型显示,从 fundus图像诊断眼的准确性很高.
科学领域:
- 眼科医生 眼科 眼科
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 玻璃眼是全球不可逆转失明的主要原因.
- 早期的玻璃眼通常缺乏明显的视力变化,这强调了及时诊断的必要性.
- 为了检测青光眼,手动分析 fundus 图像是耗时的.
研究的目的:
- 开发一个参数高效的深度学习模型,以使用 fundus 图像准确检测 DrDeramus.
- 通过结合卷积神经网络 (CNN) 和自我注意机制,提高诊断模型的通用性.
主要方法:
- 提出了一个新的AlterNet-K模型,将ResNets与多头自我注意 (MSA) 集成在一起.
- 该模型在大型鹿特丹EyePACS AIROGS数据集 (113,893个 fundus图像) 上进行了训练和验证.
- 性能与各种变压器和CNN模型进行了比较.
主要成果:
- AlterNet-K模型实现了高性能指标:准确度为0.916,AUROC为0.968,F1得分为0.915.
- 它的表现优于ViT,DeiT-S,Swin变压器,ResNet,EfficientNet,MobileNet和VGG等既有模型.
- 与MSA集成的较小的CNN显示出比较大的同行更优异的性能.
结论:
- 结合CNN和MSA的参数效率模型可以在青光眼的分类中实现高精度.
- 该AlterNet-K模型提供了一个有前途的,高效的方法,用于自动化青光眼诊断.
- 这种方法可以适应其他医学成像分类任务.
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