卷积神经网络
帕金森病
磁共振成像
人工智能
水准点(测量)
计算机科学
模式识别(心理学)
疾病
医学
放射科
病理
大地测量学
地理
作者
Pir Masoom Shah,Adnan Zeb,Uferah Shafi,Syed Farhan Alam Zaidi,Munam Ali Shah
标识
DOI:10.23919/iconac.2018.8749023
摘要
Parkinson Disease (PD) is one of the most critical progressive neurological diseases which mainly affects the motor system. The accurate diagnosis of PD has been a challenge to date, mainly due to the close relevance of PD to other neurological diseases. These close characteristics are the reasons that cause 25% inaccurate manual diagnosis of PD. In this paper, we present a Convolutional Neural Network (CNN) based automatic diagnosis system which accurately classifies PD and healthy control (HC). Parkinson's Progression Markers Initiative (PPMI) provides publically available benchmark T2-weighted Magnetic Resonance Imaging (MRI) for both PD and HC. The mid-brain slices of 500, T2-weighted MRI are selected and aligned using image registration technique. The performance of the proposed technique is evaluated using accuracy, sensitivity, specificity and AUC (Area Under Curve). The detailed comparison in the result section shows that the CNN archived a better performance from 3%-9% in terms of accuracy, sensitivity, specificity, and AUC when compared to the some existing techniques.
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