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Early Diagnosis of Parkinson’s Disease in brain MRI using Deep Learning Algorithm

机译：利用深层学习算法早期诊断帕金森病的脑MRI疾病

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The Parkinson’s disease (PD) is one of the top most prevalent degenerative disease which is caused by the loss of neurons that produce dopamine. Magnetic Resonance Imaging (MRI) is capable of capturing changes in the structure of the brain caused due to deficiency of dopamine in subjects of Parkinson’s disease. Early diagnosis of these type of diseases using computer-aided systems is an area of eminent importance and extensive research amongst researchers. Deep learning models can effectively assist the clinicians in the PD diagnosis and obtain an objective patient group classification in coming years. In this paper, detection of PD is done using deep learning algorithm to discriminate between PD and controlled subjects, which is difficult and time taking if done manually. According to research, the chance of curing increases significantly if appropriate steps are taken early and precious time could be saved if detection process is carried by a computer. By making use of the Convolutional Neural Network (CNN) and the LeNet-5 architecture, the MRI data of PD subjects was successfully classified from normal controls.
机译：帕金森病（PD）是最普遍的退行性疾病之一，这是由生产多巴胺的神经元丧失引起的。磁共振成像（MRI）能够捕获由于多巴胺缺乏在帕金森病的疾病中引起的脑部结构的变化。使用计算机辅助系统的早期诊断这些类型的疾病是研究人员中重要性和广泛的研究领域。深度学习模式可以有效地帮助临床医生在PD诊断中，并在未来几年内获得客观患者群体分类。在本文中，使用深度学习算法进行PD的检测，以区分PD和受控对象，这是手动完成的难度和时间。据研究，如果采用电脑携带的检测过程，可以节省适当的步骤，固化的机会会显着增加。通过利用卷积神经网络（CNN）和Lenet-5架构，PD受试者的MRI数据从正常控制成功分类。

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