基于迁移学习卷积神经网络的手写样本精神分裂症自动检测
Automated Schizophrenia Detection from Handwriting Samples via Transfer Learning Convolutional Neural Networks
AI总结：
本研究针对精神分裂症的手写异常特征，利用Crespo等人2019年的手写样本数据集，经预处理增强后训练多种卷积神经网络，其中InceptionV3架构模型达92%分类准确率，还开发了供医护使用的安全网站，为精神分裂症的非侵入式客观诊断提供了新路径。
摘要：
精神分裂症是一种全球流行的精神疾病，会严重损害日常生活。该疾病由大脑额纹状体通路中的多巴胺失衡引发，这种失衡会影响小脑的精细运动控制，进而导致手写行为出现异常。本研究的目标是开发一种准确、客观且易用的计算方法，用于区分精神分裂症患者与非精神分裂症人群的手写样本。为实现这一目标，研究使用了Crespo等人（2019）的数据集，该数据集包含精神分裂症患者与非患者的手写样本图像。研究对数据进行了预处理与增强，以提升模型的鲁棒性，使其能够识别不同类型的手写风格。研究使用该数据训练了多种不同的卷积神经网络，其中以InceptionV3为基础架构的模型表现最佳，区分两类图像的准确率达到92%。为提升该模型的可及性，研究开发了一个安全网站，供医疗专业人员为其患者使用。该结果表明，通过计算模型进行手写分析有望成为临床医生诊断和监测精神分裂症的一种非侵入性、客观的方法。
英文摘要：
Schizophrenia is a globally prevalent psychiatric disorder that severely impairs daily life. Schizophrenia is caused by dopamine imbalances in the fronto-striatal pathways of the brain, which influences fine motor control in the cerebellum. This leads to abnormalities in handwriting. The goal of this study was to develop an accurate, objective, and accessible computational method to be able to distinguish schizophrenic handwriting samples from non-schizophrenic handwriting samples. To achieve this, data from Crespo et al. (2019) was used, which contains images of handwriting samples from schizophrenic and non-schizophrenic patients. The data was preprocessed and augmented to produce a more robust model that can recognize different types of handwriting. The data was used to train several different convolutional neural networks, and the model with the base architecture of InceptionV3 performed the best, differentiating between the two types of image with a 92% accuracy rate. To make this model accessible, a secure website was developed for medical professionals to use for their patients. Such a result suggests that handwriting analysis through computational models holds promise as a non-invasive and objective method for clinicians to diagnose and monitor schizophrenia.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
