Khedimi Madjda, Zhang Tao, Merzougui Hanine, Zhao Xin, Geng Yanzhang, Djaroudib Khamsa, Lorenz Pascal
Department of Electrical and Information Engineering, University of Tianjin, Tianjin 300072, China.
Department of Computer Science, University of Batna 2, Batna 05078, Algeria.
Bioengineering (Basel). 2024 Dec 2;11(12):1218. doi: 10.3390/bioengineering11121218.
Parkinson's Disease (PD) is a progressive neurodegenerative disorder affecting millions worldwide. Early detection is crucial for improving patient outcomes. Spiral drawing analysis has emerged as a non-invasive tool to detect early motor impairments associated with PD. This study examines the performance of hybrid deep learning and machine learning models in detecting PD using spiral drawings, with a focus on the impact of data augmentation techniques. We compare the accuracy of Vision Transformer (ViT) with K-Nearest Neighbors (KNN), Convolutional Neural Networks (CNN) with Support Vector Machines (SVM), and Residual Neural Networks (ResNet-50) with Logistic Regression, evaluating their performance on both augmented and non-augmented data. Our findings reveal that ViT with KNN, initially achieving 96.77% accuracy on unaugmented data, experienced a notable decline across all augmentation techniques, suggesting it relies heavily on global patterns in spiral drawings. In contrast, ResNet-50 with Logistic Regression showed consistent improvement with data augmentation, reaching 93.55% accuracy when rotation and flipping techniques were applied. These results highlight that hybrid models respond differently to augmentation, and careful selection of augmentation strategies is necessary for optimizing model performance. Our study provides important insights into the development of reliable diagnostic tools for early PD detection, emphasizing the need for appropriate augmentation techniques in medical image analysis.
帕金森病（PD）是一种渐进性神经退行性疾病，影响着全球数百万人。早期检测对于改善患者预后至关重要。螺旋绘图分析已成为一种检测与PD相关的早期运动障碍的非侵入性工具。本研究考察了混合深度学习和机器学习模型在使用螺旋绘图检测PD方面的性能，重点关注数据增强技术的影响。我们将视觉Transformer（ViT）与K近邻（KNN）、卷积神经网络（CNN）与支持向量机（SVM）、残差神经网络（ResNet - 50）与逻辑回归的准确性进行比较，评估它们在增强数据和未增强数据上的性能。我们的研究结果表明，ViT与KNN最初在未增强数据上的准确率为96.77%，但在所有增强技术下均显著下降，这表明它严重依赖于螺旋绘图中的全局模式。相比之下，ResNet - 50与逻辑回归在数据增强时表现出持续的提升，在应用旋转和翻转技术时准确率达到93.55%。这些结果突出表明混合模型对增强的反应不同，谨慎选择增强策略对于优化模型性能是必要的。我们的研究为开发用于早期PD检测的可靠诊断工具提供了重要见解，强调了医学图像分析中适当增强技术的必要性。
