基于MobileNetv3卷积神经网络的轻量级光盘和光杯细分
Yuanqiong Chen1,2, Zhijie Liu2, Yujia Meng2
1School of Computer Science and Engineering, Central South University, Changsha 410000, China.
Biomimetics (Basel, Switzerland)
|October 25, 2024
概括
这项研究引入了一种高效的方法,用于使用MobileNetv3对光盘 (OD) 和光杯 (OC) 进行细分,从而改善青光眼查. 这种方法可以减少计算负载和推断时间,以实现精确的OD和OC细分.
科学领域:
- 眼科医生 眼科 眼科
- 医疗成像医学成像
- 计算机视觉 计算机视觉
背景情况:
- 玻璃眼是全球导致失明的主要原因.
- 精确的光盘 (OD) 和光杯 (OC) 细分对于通过杯对盘比 (CDR) 进行青光眼查至关重要.
- 当前的深度学习方法经常受到高计算成本和缓慢推断的影响.
研究的目的:
- 为OD和OC细分提出一个高效的端到端方法.
- 为了提高计算效率,利用轻量级网络 (MobileNetv3).
- 通过精确的OD和OC细分,实现精确和快速的玻璃眼查.
主要方法:
- 利用MobileNetv3作为端到端细分网络的核心功能提取器.
- 嵌入边界分支和对抗性学习用于OD和OC的多标签细分.
- 在三个公共数据集上验证了该方法:Drishti-GS,RIM-ONE-r3和REFUGE.
主要成果:
- 在数据集中实现了OD细分 (0.974,0.966,0.962) 和OC细分 (0.900,0.875,0.880) 的高子系数.
- 与现有方法相比,显著减少了计算复杂性和推断时间.
- 证实了该方法在高效和精确的OD和OC细分方面的有效性.
结论:
- 拟议的基于MobileNetv3的方法为OD和OC细分提供了高效和准确的解决方案.
- 这种方法有可能提高青光眼查的速度和可访问性.
- 轻量级架构和对抗式学习的结合为医疗图像细分提供了有希望的方向.
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