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使用U形网络和囊网络驱动的深度学习进行了增强的糖尿病视网膜病变检测.

Govindharaj I¹, Poongodai A², Gnanajeyaraman Rajaram³

¹Assistant Professor, Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Tamil Nadu, 600062, India.

|January 13, 2025

概括

本研究介绍了一种混合人工智能模型,将UNet++和囊网络 (CapsNet) 结合起来,以改进青光眼的检测. 这种新的方法提高了光杯和光盘细分的准确性,优于现有的早期诊断方法.

关键词:

囊网络是一个囊网络.卷积神经网络是一种卷积神经网络.糖尿病视网膜病变检测检测混合的UNet++-CapsNet框架用于自动化青光眼检测.眼镜杯是指光学杯.视觉光盘是指光盘中的光盘.一个U形网络.视力障碍视力障碍视力障碍

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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:

青光眼的诊断依赖于分析眼底图像中的光盘和杯状特征.
目前的检测方法面临挑战,因为眼变化的复杂性.
人工智能为提高诊断准确性提供了有希望的途径.

研究的目的:

开发和评估一种混合人工智能模型,用于准确检测青光眼.
通过使用深度学习架构来改善光盘和杯子细分.
将混合模型的性能与现有最先进的方法进行比较.

主要方法:

开发了一种混合深度学习模型,集成了UNet++进行细分和囊网络 (CapsNet)进行分类.
视网膜图像使用直方形平衡和对比限度自适应直方形平衡 (CLAHE) 进行了预处理.
该模型在用于光杯/光盘细分和青光眼检测的基准数据集上进行了训练和验证.

主要成果:

混合UNet++和CapsNet模型在光杯和光盘细分方面表现出卓越的性能.
预处理技术显著提高了视网膜图像质量.
与传统和当前最先进的方法相比,拟议的模型实现了更高的青光眼检测准确性.

结论:

混合UNet++和CapsNet模型为改善青光眼诊断提供了一个有效的策略.
增强的图像预处理和先进的AI架构有助于更准确的检测.
这种方法有可能更早,更可靠地识别青光眼,防止视力丧失.