使用预训练的卷积神经网络和以投票为基础的分类器融合,进行多阶段绿眼的分类
Vijaya Kumar Velpula1, Lakhan Dev Sharma1
1School of Electronics Engineering, VIT-AP University, Amaravati, Andhra Pradesh, India.
Frontiers in physiology
|June 29, 2023
概括
这项研究开发了一种使用深层卷积神经网络 (CNN) 和分类器融合的自动化玻璃眼瘤检测系统. 该系统通过 fundus 图像实现了高精度的早期玻璃眼瘤检测,优于现有方法.
科学领域:
- 眼科医生 眼科 眼科
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 玻璃眼是导致不可逆转失明的主要原因.
- 早期检测对于有效的绿眼病管理至关重要.
- 目前的诊断方法耗时,可能不准确.
研究的目的:
- 开发一种用于早期发现青光眼的自动化系统.
- 使用深度学习模型来分类青光眼的各个阶段.
- 通过分类器融合来提高诊断的准确性.
主要方法:
- 使用了五个预训练的深卷积神经网络 (CNN) 模型:ResNet50,AlexNet,VGG19,DenseNet-201和Inception-ResNet-v2.
- 采用基于最大投票的分类器融合方法.
- 在四个公共数据集上验证了模型:ACRIMA,RIM-ONE,哈佛数据集 (HVD) 和Drishti.
主要成果:
- 在ACRIMA数据集上获得了99.57%的准确性和1.0的AUC.
- 在数据集中表现出高性能,AUC为0.97 (HVD),0.90 (Drishti) 和0.95 (RIM-ONE).
- 拟议的模型在早期格劳科马分类方面表现优于最先进的方法.
结论:
- 自动化青光眼分类模型有效地实现了早期青光眼检测.
- 预训练CNN和分类器融合的组合提供了卓越的性能.
- 该系统有望提高青光眼诊断的准确性和效率.
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