优化的MobileNetV3:基于深度学习的帕金森病分类,使用融合图像
Sukanya Pechetti1, Battula Srinivasa Rao1
1School of Computer Science and Engineering, VIT-AP University, Andhra Pradesh, India.
PeerJ. Computer science
|December 11, 2023
概括
这项研究引入了一种新的深度学习方法,用于早期检测帕金森病 (PD),使用优化的MobileNetV3. 该方法从手写的记录中识别PD,提高诊断能力.
科学领域:
- 神经学 神经学
- 人工智能的人工智能
- 生物医学工程 生物医学工程
背景情况:
- 帕金森病 (PD) 是一种进展性神经疾病,具有多种运动和非运动症状,通常包括早期的声乐困难.
- 准确及时诊断PD对于有效的患者管理和改善生活质量至关重要.
- 目前用于PD检测的机器学习方法通常由于手动特征提取限制而表现不佳.
研究的目的:
- 开发一个强大的深度学习模型,用于早期和准确诊断帕金森病.
- 通过优化深度学习架构来提高PD检测的分类性能.
- 解决PD诊断中传统机器学习方法的局限性.
主要方法:
- 基于MobileNetV3的深度学习模型被建议用于帕金森病的分类.
- 为了提高性能,MobileNetV3模型使用改进的矮猩猩优化算法 (IDMO) 进行了优化.
- 基于金字塔通道的特征注意网络 (PCFAN) 用于选择关键特征以进行增强分类.
主要成果:
- 拟议的优化MobileNetV3模型,结合PCFAN,在PD检测方面表现出卓越的性能.
- 该方法在PPMI和NTUA数据集上实现了高精度 (99.34%),灵敏度 (98.53%),特异性 (97.78%) 和F-score (99.12%).
- 这些结果显著优于以前用于识别帕金森病的方法.
结论:
- 开发的深度学习框架为早期诊断帕金森病提供了强大的工具.
- 整合MobileNetV3,IDMO和PCFAN提供了一个高度准确和高效的PD检测方法.
- 这种方法在改善帕金森病的临床诊断工具方面具有重大潜力.
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