自动化青光眼诊断:优化混合分类模型与改进的U-net细分
Krishnamoorthy Varadharajalu1, Logeswari Shanmugam2
1Department of Computer Science and Engineering, Sri Venkateswara College of Engineering, Chennai, India.
Network (Bristol, England)
|March 27, 2025
概括
这项研究介绍了一种使用先进图像处理和混合AI的自动化玻璃眼诊断模型. 这种新的方法在检测青光眼方面达到很高的准确性,有助于早期干预和预防失明.
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:
- 玻璃眼是全球不可逆转失明的主要原因.
- 早期检测对于有效的绿眼病管理至关重要.
- 目前的诊断方法在精确的结构细分和疾病分期方面存在局限性.
研究的目的:
- 开发一个优化的混合分类模型,用于自动化青光眼诊断.
- 解决细分小眼结构和分类青光眼病阶段的挑战.
- 为了提高青光眼检测系统的准确性和效率.
主要方法:
- 使用对比度有限的自适应性直方体平衡 (CLAHE) 进行预处理.
- 改进了U-Net细分,具有新的交叉损失函数.
- 提取各种特征:分形,杯到盘,基于ISNT规则的,以及改进的Pyramid Histogram of Orient Gradient (PHOG).
- 混合分类使用改进的卷积神经网络 (ICNN) 和优化的循环神经网络 (RNN).
- 通过基于对立的学习支持的纳米布甲虫优化 (OBL-NBO) 来优化RNN权重.
主要成果:
- 拟议的OBL-NBO优化模型实现了高诊断准确度.
- 在80%的培训数据中,数据集1的准确率为0.927和数据集2的准确率为0.945.
- 混合ICNN和优化的RNN模型在分类青光眼中表现出强大的性能.
结论:
- 开发的自动化系统为准确的青光眼诊断提供了一个有希望的方法.
- 集成先进的人工智能技术和特征提取方法可以提高诊断能力.
- 这项研究有助于开发有效的工具,用于早期发现和管理青光眼,从而有可能降低失明率.
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