基于混合特征提取和深度学习方法的灰狼优化使用增强的磁盘的分类
Yasemin Sarı1, Nesrin Aydın Atasoy2
1The Institute of Graduate Programs, Karabük University, Karabük 78050, Türkiye.
概括
本研究引入了一种使用ResNet50和灰狼优化 (GWO) 的自动化方法,用于从MRI中分类腰椎间盘. 混合方法显著提高了这种常见疾病的诊断准确性.
科学领域:
- 医学成像分析 医学成像分析
- 医疗保健中的人工智能
- 脊柱诊断 脊柱诊断 脊柱诊断 脊柱诊断
背景情况:
- 由于长时间使用电脑,腰椎间盘的发生率越来越高.
- 早期诊断和治疗对于有利的结果至关重要.
- 需要对腰椎间盘的自动,准确的分类.
研究的目的:
- 开发一种计算机辅助的,完全自动化的方法,使用MRI对腰椎间盘的分类.
- 通过先进的机器学习技术来提高诊断准确性和效率.
主要方法:
- 混合方法结合了剩余网络 (ResNet50) 进行特征提取和灰狼优化 (GWO) 进行特征选择.
- 使用多层感知器 (MLP) 和支持矢量机 (SVM) 分类器,具有各种用于分类的功能.
- 采用深度学习来实现强大的特征表示和优化算法,以提高分类性能.
主要成果:
- 拟议的ResNet50-GWO-SVM方法论在准确性,精度,回忆和F1得分方面取得了显著的改善.
- 在分类腰椎间盘的传统方法中表现优于传统方法.
- 验证了将深度学习特征提取与优化和机器学习分类器集成的有效性.
结论:
- 与CapsNet,EfficientNetB6和DenseNet169.9相比,ResNet50-GWO-SVM的方法取得了更高的性能.
- 在腰椎间盘的分类任务中证明了强度和有效性.
- 突出了人工智能驱动工具的潜力,以改善脊柱诊断.
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