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使用光卷积神经网络和内在模式功能技术识别视网膜疾病

Preethi Kulkarni¹, Konda Srinivasa Reddy¹

¹School of Computer Science and Engineering, VIT-AP University, Amaravathi 522241, Andhra Pradesh, India.

Diagnostics (Basel, Switzerland)

|March 14, 2026

概括

结合实证模式分解 (EMD) 过与光卷积神经网络 (LightCNN) 的新混合模型显著改善了从 fundus 图像中自动诊断视网膜疾病.

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:

眼底成像对于早期检测视网膜疾病至关重要.
自动翻译面临着图像变化和噪音的挑战.
准确的诊断依赖于强大的图像分析技术.

研究的目的:

开发一种新的混合模型,用于增强 fundus 图像分类.
提高视网膜疾病自动诊断的准确性和效率.
为了解决当前自动化 fundus 图像分析的局限性.

主要方法:

一个混合模型,集成内在模式函数 (IMF) 过 (从经验模式分解) 与光卷积神经网络 (LightCNN).
国际货币基金组织 (IMF) 的过预处理了基底图像,以减少噪音和保存视网膜图案.
轻CNN在精细的图像组件上进行轻量级特征提取和分类.

主要成果:

在DIARETDB数据集上,IMF + LightCNN模型实现了99.4%的准确性,99.1%的精度,98.87%的回忆率和98.31的F1得分.
与传统的卷积神经网络 (CNN) 和基于ResNet的模型相比,其表现优越.
在诊断准确度和计算效率方面显著改善.

结论:

将先进的信号处理 (IMF过) 与轻量级深度学习 (LightCNN) 集成,可以提高诊断的准确性.
混合框架为可靠的视网膜疾病实时临床查提供了一个有希望的方法.
这种方法提高了诊断性能和基础图像分析的计算效率.

关键词:

糖尿病视网膜病变 - 糖尿病视网膜病变内在模式功能的内在模式功能轻度卷积神经网络轻度卷积神经网络基金图片基金图片基金图片

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