一个深度特征融合改进的疑似角质检测与深度学习
Ali H Al-Timemy1, Laith Alzubaidi2,3, Zahraa M Mosa4
1Biomedical Engineering Department, Al-Khwarizmi College of Engineering, University of Baghdad, Baghdad 10011, Iraq.
Diagnostics (Basel, Switzerland)
|May 27, 2023
概括
这项研究引入了一个深度学习 (DL) 模型,用于早期检测角 (KCN). 人工智能模型使用角膜地图准确识别亚临床和已建立的KCN,提高诊断能力.
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:
- 即使对于专家来说,早期发现角 (KCN) 也很困难.
- 准确诊断亚临床KCN对于及时干预至关重要.
研究的目的:
- 开发和验证深度学习 (DL) 模型,用于检测早期和已确定的角 (KCN).
- 为了提高KCN检测的准确性和稳定性,使用化角膜地图特征.
主要方法:
- 利用Xception和InceptionResNetV2深度学习架构从角膜地图中提取特征.
- 从DL模型中融合特征,以增强亚临床KCN检测.
- 在来自埃及 (1371只眼睛) 和伊拉克 (213只眼睛) 的数据集上训练并验证了模型.
主要成果:
- 在埃及数据集中实现了0.99和97-100%的ROC曲线下的面积 (AUC).
- 在独立的伊拉克数据集中验证了0.91-0.92的AUC和88-92%的准确性.
- 在区分正常眼睛和患有亚临床和已确定的KCN的眼睛方面表现出很高的性能.
结论:
- 拟议的DL模型对改善临床和亚临床角膜的检测有显著的前景.
- 这种人工智能驱动的方法为诊断KCN提供了更准确和更强大的方法.
- 这些发现表明,对于角膜疾病的眼科诊断工具的潜在进步.
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