在世界上，约有七百万到一千万老年人正在承受着帕金森（Parkinson's Disease，PD）疾病带来的困扰.帕金森疾病是一种常见的神经系统变性疾病，它的临床特征为震颤、强直、运动迟缓以及自主能力的下降.其临床表现和多系统萎缩（Multiple System Atrophy，MSA）病症极为相似.研究表明，帕金森病症患者在确诊时，往往已经到了无法挽回的境地，所以对于帕金森病症能够区别于MSA病症并且得到早期诊断，人们在不断探索新的方法.随着大数据时代的到来，深度学习在图像识别和分类方面取得了重大性突破.所以，本研究提出使用深度学习方法实现对帕金森疾病、多系统萎缩症和健康人群的诊断.本数据来源北京301医院.原始核磁共振图像（Magnetic Resonance Image，MRI）的处理得到北京301医院医生的指导.本实验重点在于改进现有神经网络，使其在医学图像识别和诊断中获得良好的效果.本实验依据帕金森病症的病理特点提出了改进算法，通过对比模型损失、准确率等指标获得了较好的实验结果.
In the world, about seven to ten million elderly people are suffering from the Parkinson's Disease (PD). PD is a common degenerative nervous system disease. Its clinical characters are tremor, muscle rigidity, bradykinesia, and the degression of independent ability. These characters are similar with the Multiple System Atrophy (MSA). Research shows that patients with PD are often irreparably diagnosed, so people are constantly exploring new ways to differentiate PD with MSA and get early diagnosis. With the advent of the big data era, deep learning has made major breakthroughs in image recognition and classification. Therefore, the study uses the deep learning methods to differentiate PD, MSA, and healthy people. The data is from 301 Hospital of Beijing. The pre-treatment of the original Magnetic Resonance Image (MRI) is directed by the physicians of 301 Hospital of Beijing. The focus of this experiment is to optimize the neural network and make it get good results in medical image recognition and diagnosis. Based on the pathological characteristics of PD, the study proposed an improved algorithm, and it gets the better experimental results in loss, accuracy, and other indicators.