Parkinson’s Disease Detection Using Spiral Images (Hand Drawings)
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Parkinson’s disease (PD), or simply Parkinson’s, is a long-term degenerative disorder of the central nervous system that mainly affects the motor system. The symptoms usually emerge slowly, and as the disease worsens, non-motor symptoms become more common. The most obvious early symptoms are tremor, rigidity, slowness of movement, and difficulty with walking. Parkinson’s Disease can cause slow motor movements, depression, anxiety, sleep and sensory system disorders, behaviour changes etc. Environmental factors and Genetic Inheritance are amongst the major contributing factors for Parkinson’s disease. Spiral drawing is a skilled and complex coordinated motor activity. Therefore, it is treated as a sensitive motor assessment and a preliminary test for early symptoms of Parkinson’s disease. This article presents a solution for detecting Parkinson’s disease using Spiral Drawings and Convolutional Neural Networks (CNN). The Cainvas Platform is used for implementation, which provides seamless execution of python notebooks for building AI systems that can eventually be deployed on edge (i.e. an embedded system such as compact MCUs). The notebook can be found here. The flow of the article is as follows — Description of the Problem Statement The Dataset Data Augmentation CNN Model Architecture Training the Model Performance of Model Testing models on Images Conclusion Description of the Problem Statement The project aims at presenting a solution for Parkinson’s disease detection using Spiral Drawings and CNN. The main idea behind the implementation is to classify a person as Healthy or having Parkinson’s disease by looking at the Spiral Drawing made by the person. The Spiral Drawing created by a healthy person will look almost similar to a standard spiral shape. However, a spiral drawn by a person with Parkinson’s disease will highly deviate from a perfect spiral shape and look distorted due to slow motor movements and decreased coordination between hand and brain. The Dataset The dataset used here is the Parkinson’s Drawing Dataset present on Kaggle. The dataset can be accessed through this link. The dataset contains Spiral and Waves drawings made by healthy people and Parkinson’s disease infected people. In this article, only Spiral Drawings are used for classification. The dataset already contains the Train Set and the Test Set, so no need to manually split the dataset.
帕金森病（Parkinson’s disease, PD，简称帕金森症）是一种长期进展性中枢神经系统（central nervous system）退行性疾病，主要累及运动系统（motor system）。该疾病的症状通常隐匿起病，随病情进展，非运动症状愈发常见。最显著的早期症状包括震颤、肌强直、运动迟缓以及行走困难。帕金森病还可引发运动迟缓、抑郁、焦虑、睡眠与感觉系统障碍、行为改变等多种表现。环境因素与遗传因素是帕金森病的主要致病诱因之一。 螺旋绘画是一项兼具技巧性与复杂性的协同运动活动，因此被视为敏感的运动评估手段，可作为帕金森病早期症状的初步筛查测试。本文提出了一种基于螺旋绘画与卷积神经网络（Convolutional Neural Networks, CNN）的帕金森病检测方案。本研究采用Cainvas平台开展实现，该平台可无缝运行Python笔记本，用于构建最终可部署至边缘端（edge）的人工智能系统，边缘端即紧凑型微控制单元（Microcontroller Unit, MCU）等嵌入式系统（embedded system）。相关Python笔记本可在此处获取。 本文的行文脉络如下：问题陈述概述 → 数据集说明 → 数据增强 → CNN模型架构 → 模型训练 → 模型性能评估 → 图像模型测试 → 结论。 问题陈述概述 本项目旨在提出一种基于螺旋绘画与CNN的帕金森病检测方案。核心思路为通过受试者绘制的螺旋绘画，将其分类为健康人群或帕金森病患者。健康人群绘制的螺旋图形与标准螺旋形态高度相似，而帕金森病患者由于运动迟缓、手脑协调能力下降，其所绘制的螺旋图形会严重偏离标准螺旋形态，呈现扭曲失真的状态。 数据集说明 本研究使用的数据集为Kaggle平台上的帕金森绘画数据集（Parkinson’s Drawing Dataset），可通过此链接获取。该数据集包含健康人群与帕金森病患者绘制的螺旋绘图与波浪绘图，本文仅采用螺旋绘图开展分类任务。该数据集已预先划分为训练集与测试集，无需手动进行数据集拆分。
