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使用ANFIS优化CNN在缺血再输液期间监测脏微解剖学
Niranjana Devi Balakrishnan1, Suresh Kumar Perumal2
1Department of Biomedical Engineering, Paavai Engineering College, Namakkal, Tamil Nadu, India. niranjanadevi2024b@gmail.com.
International urology and nephrology
|March 18, 2025
概括
使用自适应性神经模糊推断系统-Resnet50卷积神经网络 (ANFIS-CNN) 的新型深度学习方法,从光学连贯断层扫描 (OCT) 图像准确地监测脏疾病,实现高精度和回忆.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 脏病理学 脏病理学
背景情况:
- 病对健康构成重大风险,早期发现对于有效管理至关重要.
- 通过光学连贯断层扫描 (OCT) 图像监测脏微生物学变化,有助于了解疾病进展,特别是在缺血-再输液期间.
- 目前用于识别病的图像分析方法往往缺乏精度,因为特征相关性分析的局限性.
研究的目的:
- 开发一种高精度的深度学习 (DL) 模型,用于使用OCT图像监测病.
- 与现有方法相比,提高病检测的分类准确性,回忆力和精度.
- 引入基于自适应的神经模糊推理系统的Resnet50最佳卷积神经网络 (ANFIS-CNN) 以增强图像分析.
主要方法:
- 使用来自病分析标准存储库的OCT图像.
- 应用双向和高斯过用于图像预处理,以减少噪音和识别脏结构.
- 采用基于边缘的细分与图表平衡用于颜色密度分析和对象识别,结合递归光谱多尺度特征选择 (RSMFS).
主要成果:
- 拟议的ANFIS-CNN方法实现了高分类精度 (96.1%),回忆 (95.18%) 和精度 (96.09%).
- 该系统通过高性能图像识别来识别病的性能提高.
- 使用ANFIS-Resnet50 CNN优化的光谱值显著提高了分类准确性.
结论:
- ANFIS-CNN方法提供了一种强大而准确的方法,用于使用OCT成像监测脏疾病.
- 这种深度学习模型显著提高了病检测的精度和回忆.
- 开发的系统有望通过先进的图像分析和早期疾病识别来改善患者的治疗结果.
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