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从视网膜图像使用深度学习方法自动诊断玻璃眼

Ayesha Shoukat¹, Shahzad Akbar¹, Syed Ale Hassan¹

¹Department of Computer Science, Riphah International University, Faisalabad Campus, Faisalabad 44000, Pakistan.

Diagnostics (Basel, Switzerland)

|May 27, 2023

概括

早期发现青光眼对于预防失明至关重要. 一种新的深度学习方法可以准确地识别视网膜图像中的微妙模式,帮助及时诊断和干预.

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,以高眼内压和视神经损伤为特征,可导致不可逆转的失明.
早期检测至关重要,但老年人中青光眼常常被诊断为晚期,因此需要改进诊断方法.
目前的手动眼评估是耗时的,昂贵的,需要专业技能,实验技术尚未提供最终解决方案.

研究的目的:

开发和验证一种基于深度学习的自动化方法,用于准确的早期青光瘤检测.
在视网膜图像中识别可能被人类临床医生忽视的微妙模式.

主要方法:

使用灰色通道的 fundus 图像进行分析.
采用数据增强来创建用于培训的多样化数据集.
使用ResNet-50架构训练了一个卷积神经网络 (CNN) 模型.

主要成果:

在多个数据集 (G1020,RIM-ONE,ORIGA,DRISHTI-GS) 中检测早期青光眼的高精度.
在G1020数据集上,该模型显示了98.48%的准确性,99.30%的灵敏性,96.52%的特异性,97%的AUC和98%的F1分数.
深度学习方法成功地识别了手工评估中经常错过的模式.

结论:

拟议的深度学习模型提供了一个高度准确和自动化的解决方案,用于早期发现青光眼.
这种方法有可能显著帮助临床医生及时诊断和干预,防止视力丧失.

关键词:

增强增强增强增强深度学习是一种深度学习.在早期阶段进行检测.基金图片基金图片基金图片玻璃眼 glaucoma 玻璃眼玻璃眼玻璃眼玻璃眼

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