论文标题
基于深度学习的帕金森病早期诊断
Deep Learning Based Early Diagnostics of Parkinsons Disease
论文作者
论文摘要
在世界上，大约7至1000万老人患有帕金森氏病（PD）疾病。帕金森氏病是一种常见的神经性退化性疾病，其临床特征是震颤，僵化，胸肌症和自主权降低。它的临床表现与多种系统萎缩（MSA）疾病非常相似。研究表明，帕金森氏病的患者在被诊断时通常会达到无法弥补的情况，因此，由于帕金森氏病可以与MSA疾病区分开并得到早期诊断，因此人们一直在探索新方法。随着大数据时代的出现，深度学习在图像识别和分类方面取得了重大突破。因此，本研究建议使用深度学习方法来实现帕金森氏病，多系统萎缩和健康的人的诊断。该数据源来自伊斯坦布尔大学塞拉帕萨医学院。原始磁共振图像（MRI磁共振图像，MRI）的处理由伊斯坦布尔大学Cerrahpasa医学院医院的医生指导。该实验的重点是改善现有的神经网络，以便在医学图像识别和诊断方面获得良好的结果。根据帕金森氏病的病理特征提出了改进的算法，并通过比较诸如模型丢失和准确性等指标获得了良好的实验结果。
In the world, about 7 to 10 million elderly people are suffering from Parkinson's Disease (PD) disease. Parkinson's disease is a common neurological degenerative disease, and its clinical characteristics are Tremors, rigidity, bradykinesia, and decreased autonomy. Its clinical manifestations are very similar to Multiple System Atrophy (MSA) disorders. Studies have shown that patients with Parkinson's disease often reach an irreparable situation when diagnosed, so As Parkinson's disease can be distinguished from MSA disease and get an early diagnosis, people are constantly exploring new methods. With the advent of the era of big data, deep learning has made major breakthroughs in image recognition and classification. Therefore, this study proposes to use The deep learning method to realize the diagnosis of Parkinson's disease, multiple system atrophy, and healthy people. This data source is from Istanbul University Cerrahpasa Faculty of Medicine Hospital. The processing of the original magnetic resonance image (Magnetic Resonance Image, MRI) is guided by the doctor of Istanbul University Cerrahpasa Faculty of Medicine Hospital. The focus of this experiment is to improve the existing neural network so that it can obtain good results in medical image recognition and diagnosis. An improved algorithm was proposed based on the pathological characteristics of Parkinson's disease, and good experimental results were obtained by comparing indicators such as model loss and accuracy.
