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使用EfficientNetB3架构进行基于图像的眼部疾病检测
Rahaf Alsohemi1, Samia Dardouri1,2
1Department of Computer Science, College of Computing and Information Technology, Shaqra University, Shaqra 11911, Saudi Arabia.
Journal of imaging
|August 27, 2025
概括
一种深度学习模型可以从眼底图像中准确地分类视网膜疾病,如糖尿病视网膜病,白内障和绿内障, 达到95.12%的准确率. 这种自动化方法有助于早期诊断和预防视力损失.
科学领域:
- 眼科 眼科
- 计算机科学
- 人工智能
背景情况:
- 早期发现视网膜疾病对于预防视力丧失至关重要.
- 手动诊断 fundus 图像是耗时且容易出现错误的.
- 需要自动化解决方案来提高诊断效率和准确性.
研究的目的:
- 开发和评估用于视网膜疾病自动分类的深度学习模型.
- 将 fundus 图像分为四个类别:白内障,糖尿病视网膜病变,绿内障和健康.
- 使用各种分类指标评估模型的性能.
主要方法:
- 使用预训练的 EfficientNetB3 架构进行图像分类.
- 在公开的Kaggle视网膜图像数据集上微调模型.
- 采用转移学习,数据增强,以及使用同位素化调度器的Adam优化器.
主要成果:
- 获得了高分类准确度的95.12%.
- 在精度 (95.21%),回忆 (94.88%),F1得分 (95.00%),子得分 (94.91%),贾卡德指数 (91.2%) 和MCC (0.925) 中表现出强的表现.
- 该模型在分类四种不同的视网膜疾病方面表现出强大.
结论:
- 拟议的深度学习模型显示了视网膜疾病自动诊断的巨大潜力.
- 这种自动化系统可以支持临床决策并改善患者的治疗结果.
- 需要在临床环境中进一步验证其实用性.
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