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使用HCCNN与PSO驱动的超参数调整优化了使用HCCNN进行青光眼的检测

Latha G¹, Aruna Priya P¹

¹Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Kattankulathur-603203, Chengalpattu District, Tamil Nadu, India.

Biomedical physics & engineering express

|April 7, 2025

概括

这项研究介绍了一种混合中心卷积神经网络 (HCCNN),通过粒子群优化 (PSO) 优化,以准确检测青光眼. 该系统还对光盘和光杯区域进行细分,以确定青光眼的严重程度.

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:

玻璃眼是全球不可逆转的失明的主要原因.
早期发现和准确的诊断对于有效的管理至关重要.
自动化系统可以帮助临床医生从 fundus 图像中识别 DrDeramus.

研究的目的:

使用混合中心卷积神经网络 (HCCNN) 开发一种有效的青光眼检测系统.
通过粒子集群优化 (PSO) 提高分类准确性并降低计算复杂性.
用修改的U-Net.来对视觉盘 (OD) 和视觉杯 (OC) 区域进行细分,以评估绿眼病的严重程度.

主要方法:

从 fundus 图像中提取特征,使用建议的 HCCNN 模型.
使用PSO进行超参数优化 (放弃率,学习率,密集层神经元),以提高性能.
使用修改后的U-Net进行严重性分析,对OD和OC区域进行道分离和细分.

主要成果:

在DRISHTI-GS和RIM-ONE数据集上,PSO-HCCNN模型分别实现了94%和97%的分类准确性.
在早期发现青光眼的准确性,灵敏度,特异性和AUC方面显著改善.
用 Dice 系数和 Jaccard 指数评估细分性能.

结论:

将PSO与HCCNN集成优化了参数,从而形成了一个强大而精确的青光眼分类模型.
准确的OD和OC细分有助于确定青光眼的严重程度.
拟议的方法在临床实践中显示了早期和准确的青光眼诊断的潜力.

关键词:

在HCCNN中,我们可以看到HCCNN.公共服务人员 (PSO)深度学习是一种深度学习.图片来源: 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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