妇科卫生保健:通过进化引力神经认知神经网络优化游牧民族优化器揭示盆腔质量分类
1Department of Biomedical Engineering, Hindusthan College of Engineering and Technology, Coimbatore 641032, India.
Diagnostics (Basel, Switzerland)
|October 14, 2023
概括
一种新的深度学习方法,即以游牧民族优化器 (EGNNN-NPOA-PM-UI) 优化的进化引力新认知神经网络,准确地分类了妇科骨盆质量. 该方法改进了现有的早期检测和诊断技术.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 妇科 妇科医生 妇科
背景情况:
- 早期检测恶性盆腔质量对于及时干预和改善患者结果至关重要.
- 对于盆腔质量分类的现有深度学习方法往往缺乏足够的准确性和效率.
- 计算时间和准确性是分类妇科腹部盆腔质量的关键挑战.
研究的目的:
- 提出一个准确和高效的深度学习模型来分类妇科腹部盆腔质量.
- 在准确性和计算时间方面克服现有方法的局限性.
- 引入进化引力新认知子神经网络,优化了游牧民族优化器 (EGNNN-NPOA-PM-UI) 用于盆腔质量分类.
主要方法:
- 超声波盆腔质量图像使用随机转换来增强.
- 兴趣区域 (ROI) 提取使用基于3D Tsallis的多层值进行.
- 特性提取利用了带有包裹的快速离散曲线变换 (FDCT-WRP).
- 使用优化了游牧民族优化器 (NPOA) 的EGNNN实现了分类.
主要成果:
- 拟议的EGNNN-NPOA-PM-UI方法实现了99.8%的分类准确性.
- 该方法在盆腔质量分析中与现有技术相比,表现优越.
- 实时超声波图像被有效地用于分类.
结论:
- 该EGNNN-NPOA-PM-UI方法提供了一个高度准确和高效的方法,用于盆腔质量分类.
- 这种人工智能驱动的方法可以显著帮助早期和准确的预测盆腔质量.
- 拟议的方法显示了改善妇科成像诊断能力的希望.
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