Detecting Parkinson's Disease with Image Classification

S. Kanagaraj, M. Hema, M. Guptha, V. Namitha
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引用次数: 16

Abstract

The non-curable neurological disorder that affects the motor system is known as Parkinson disease. When Parkinson disease is detected earlier, then it can diagnose, and we can get a quick relief but not permanent. The neurons segregate a chemical called dopamine. That helps for transmitting the signs to the other neurons in the brain. When the dopamine flow starts to fall, then the PD occurs. This makes the patients to, resting tremors, bradykinesia and rigidity problems. Here machine-learning dramatizations position in patterns tag in biomedical sciences. The PD mainly attack the motor system so that can be analysed by the Magnetic Resonance Imaging (MRI) scan, one can detect and predict the disease. In this paper, with MRI scan the Parkinson's disease is detected by using CNN, VGG-16 model and ResNET-50. The VGG-16 and ResNet-50 are compared and find the best model based on the accuracy.
用图像分类检测帕金森病
这种影响运动系统的不可治愈的神经系统疾病被称为帕金森病。当帕金森氏症被早期发现时,它就可以被诊断出来,我们可以得到快速的缓解,但不是永久的。神经元分离出一种叫做多巴胺的化学物质。这有助于将信号传递给大脑中的其他神经元。当多巴胺流量开始下降时,PD就发生了。这使得患者静息时震颤、运动迟缓和僵硬等问题。在这里,机器学习戏剧化在生物医学科学的模式标签中占有一席之地。PD主要攻击运动系统,因此可以通过磁共振成像(MRI)扫描进行分析,从而检测和预测疾病。本文采用CNN、VGG-16模型和ResNET-50进行MRI扫描检测帕金森病。比较了VGG-16和ResNet-50模型的精度,找到了最佳模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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