深度学习增强的糖尿病视网膜病变图像分类
Ghadah Alwakid1, Walaa Gouda2, Mamoona Humayun3
1Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakakah, Saudi Arabia.
Digital health
|August 17, 2023
概括
一个深度学习模型准确地识别了糖尿病视网膜病变 (DR) 的阶段. 增强图像数据改善了模型性能,这对于防止DR不可逆转的视力损失至关重要.
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:
- 糖尿病视网膜病变 (DR) 是可预防的失明的主要原因.
- 早期发现和治疗DR对于保护视力至关重要.
- 深度学习 (DL) 为自动化DR选提供了潜力.
研究的目的:
- 开发和评估一个DL模型,准确分类糖尿病视网膜病变的五个阶段.
- 评估图像增强技术对DR检测DL模型性能的影响.
主要方法:
- 为DR阶段识别实施了DenseNet-121深度学习模型.
- 两个数据集,APTOS和DDR,用于模型培训和验证.
- 图像增强技术用于提高数据集质量和平衡.
主要成果:
- 拟议的DL模型在两个数据集上都实现了高精度:APTOS上的测试精度为98.36%,DDR上的测试精度为79.67%.
- 在DDR数据集上,Top-2和Top-3准确度分数分别超过92%和98%.
- 精度,回忆和F1分数指标证实了该模型在DR阶段分类中的有效性.
结论:
- 深度学习模型可以有效地识别糖尿病视网膜病变的多个阶段.
- 使用更高质量的增强图像可以显著提高DL模型的性能.
- 这种方法有望提高DR查效率和准确性.
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