Abstract
Parkinson's disease (PD) is a progressive neurodegenerative condition characterized by the death of dopaminergic neurons, leading to various movement disorder symptoms. Early diagnosis of PD is crucial to prevent adverse effects, yet traditional diagnostic methods are often cumbersome and costly. In this study, a machine learning-based approach is proposed using hand-drawn spiral and wave images as potential biomarkers for PD detection. Our methodology leverages convolutional neural networks (CNNs), transfer learning, and attention mechanisms to improve model performance and resilience against overfitting. To enhance the diversity and richness of both spiral and wave categories, the training dataset undergoes augmentation to increase the number of images. The proposed architecture comprises three phases: utilizing pre-trained CNNs, incorporating custom convolutional layers, and ensemble voting. Employing hard voting further enhances performance by aggregating predictions from multiple models. Experimental results show promising accuracy rates. For spiral images, weighted average precision, recall, and F1-score are 90%, and for wave images, they are 96.67%. After combining the predictions through ensemble hard voting, the overall accuracy is 93.3%. These findings underscore the potential of machine learning in early PD diagnosis, offering a non-invasive and cost-effective solution to improve patient outcomes.
Abstract (translated)
帕金森病（PD）是一种进行性的神经退行性疾病，其特点是多巴胺能神经元的死亡，导致各种运动障碍症状。早期诊断帕金森病对于防止不良后果至关重要，然而传统的诊断方法往往繁琐且昂贵。在这项研究中，提出了一种基于机器学习的方法，使用手绘螺旋和波形图像作为潜在生物标志物来检测PD。我们的方法利用卷积神经网络（CNN）、迁移学习以及注意力机制来提高模型性能并增强其对过拟合的抵抗能力。为了增加训练数据集的多样性和丰富性，通过数据扩充技术增加了图像的数量。 所提出的架构包括三个阶段：使用预训练的CNN、添加自定义卷积层和集成投票。利用硬投票进一步提高了性能，通过汇总多个模型的预测结果来实现。实验结果显示了令人鼓舞的准确率。对于螺旋图像，加权平均精确度、召回率和F1分数分别为90%，而对于波形图像则为96.67%。经过集成硬投票组合预测后，整体准确性达到了93.3%。 这些发现强调了机器学习在早期PD诊断中的潜力，提供了一种非侵入性且成本效益高的解决方案，以改善患者的预后情况。
URL
https://arxiv.org/abs/2510.05015
