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提高青光眼的诊断:在合成图像和分类中使用预训练的MobileNetV2进行生成对抗网络

I Govindharaj¹, D Santhakumar², K Pugazharasi³

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

|January 15, 2025

概括

这项研究引入了一种使用生成对抗网络 (GAN) 和MobileNetV2用于早期青光眼诊断的新方法. 该方法在从 fundus 图像中分类眼时达到很高的准确性,提高了早期检测率.

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:

玻璃眼是不可逆转的视力丧失的主要原因,在早期阶段往往无症状.
准确和早期诊断至关重要,但与传统方法具有挑战性.
医疗成像数据集对于玻璃眼经常遭受不平衡和小样本大小.

研究的目的:

开发和验证一种新的深度学习方法,用于准确的青光眼诊断.
用生成对抗网络 (GAN) 来解决医学成像数据集中的数据稀缺性和质量问题.
通过使用预训练的MobileNetV2模型,提高眼查的分类准确性.

主要方法:

使用生成对抗网络 (GAN) 合成高质量,现实的基金图像,增强数据集多样性.
用高斯过来降低 fundus 图像中的噪音.
一个增强的水平设置算法被用于精确的光学杯分段.
一个预训练的MobileNetV2架构被微调,用于对正常与绿眼镜图像的二进制分类.

主要成果:

拟议的方法在区分正常和青光眼图像方面实现了98.9%的高分类准确度.
GAN有效地生成了高质量的基金图像,减轻了数据集不平衡.
增强的水平设置算法提供了对玻璃眼特征的准确细分.

结论:

开发的方法表明了GAN和转移学习在医疗成像中对玻璃眼查的显著潜力.
这种人工智能驱动的方法为早期和准确的青光眼诊断提供了一个有前途的工具,有可能减少视力丧失.
该研究强调了结合先进的图像生成,细分和深度学习分类技术的有效性.

关键词:

增强的水平设定算法算法底部图像成像底部图像成像用GAN增强的MobileNetV2用于眼的检测.生成性的对抗性网络.青光眼的检测检测仪根据MobileNetV2的分类,移动网络V2的分类.光学杯的细分是指光学杯的细分视力损失视力损失的原因

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