基于深度学习的帕金森疾病多病程诊断网络模型

Deep Learning-Based Parkinson’s Disease Multi-Stage Diagnostic Systems

摘要: 帕金森(Parkinson’s Disease, PD)是一种神经退行性疾病。当出现明显临床特征时，超过60%的黑质神经元已经发生不可逆的退化，早期疾病的诊断尤为关键，但疾病早期的诊断存在特异性不强、特征不明显等问题。因此，本文提出一种基于深度学习的帕金森疾病辅助诊断系统。首先，运用图像分割和生成对抗网络对原始图像进行分割和扩容；其次，引入多尺度卷积和注意力机制改进MobileNetV2网络，对PD、正常组以及特征不明显的前驱体的多病程诊断任务中，诊断准确率达到了92.4%，精确度达到91.7%，召回率达到92.4%，模型表现优于其他经典网络模型，且更聚焦帕金森病理学特征区域，具有更准确可靠的临床诊断效能。

Abstract: Parkinson’s Disease (PD) is a neurodegenerative disorder. When clinical features become evident, over 60% of the substantia nigra neurons have undergone irreversible degeneration. Early diagnosis of the disease is particularly crucial; however, challenges such as low specificity and inconspicuous features exist in the early detection of the disease. Therefore, this study proposes a deep learning-based Parkinson’s disease auxiliary diagnostic system. Firstly, image segmentation and generative adversarial networks are employed to segment and augment original images. Subsequently, multi-scale convolution and attention mechanisms are introduced to enhance the MobileNetV2 network for multi-stage diagnosis tasks involving PD, normal groups, and indistinct precursor features. The diagnostic accuracy achieved 92.4%, precision reached 91.7%, and recall reached 92.4%. The model outperforms other classical network models and focuses more on the pathological features of Parkinson’s disease, demonstrating more accurate and reliable clinical diagnostic efficacy.