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使用深度学习模型增强的元启发算法来诊断帕金森病的改进方法

Babita Majhi¹, Aarti Kashyap¹, Siddhartha Suprasad Mohanty¹

¹Department of CSIT, Central University, Guru Ghasidas Vishwavidyalaya, Bilaspur, Chhattisgarh, 495009, India.

BMC medical imaging

|June 23, 2024

概括

这项研究引入了先进的深度学习模型,用于使用医学成像来早期检测帕金森病 (PD). 混合模型的准确度超过99%,显著提高了早期诊断能力.

科学领域:

神经学神经学
人工智能的人工智能
医疗成像医学成像

背景情况:

早期诊断帕金森病 (PD) 仍然是一个临床挑战.
像MRI和SPECT这样的医学成像技术提供了非侵入性的定量大脑健康测量.
机器和深度学习模型对于使用成像数据进行准确的PD诊断至关重要.

研究的目的:

提出和评估深度学习模型,包括混合方法,用于早期检测帕金森病.
通过使用灰狼优化 (GWO) 的超参数优化来增强模型性能.

主要方法:

四个深度学习模型和一个混合模型被开发用于PD检测.
灰狼优化 (GWO) 用于自动超参数调整.
模型应用于T1,T2加权的MRI和SPECT DaTscan数据集.

主要成果:

所有提出的模型都表现出高性能,准确度接近或超过99%.
混合GWO-VGG16+InceptionV3模型在T1,T2加权数据集上实现了99.94%的准确性和99.99%的AUC.
在SPECT DaTscan数据集上,GWO-VGG16+InceptionV3模型实现了100%的准确性和99.92%的AUC.

结论:

深度学习模型,特别是混合GWO-VGG16+InceptionV3模型,在早期发现帕金森病方面表现出卓越的有效性.
使用GWO的优化深度学习模型显著提高了使用医学成像的PD诊断准确度.
这些发现表明,改善早期PD诊断和患者管理是一个有希望的途径.

关键词:

深度学习是一种深度学习.灰狼优化优化灰狼优化开始V3 开始V3 帕金森病是帕金森氏症的一种疾病.在SPECT DaTscan中使用.在T1,T2中加权.在VGG16中,VGG16是VGG16中的一个.

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