论文标题
将深度学习应用于特定的学习障碍筛查
Applying Deep Learning to Specific Learning Disorder Screening
论文作者
论文摘要
早期检测是治疗被诊断患有特定学习障碍的人的关键，其中包括拼写，语法，标点符号，清晰度和书面表达的组织。及早介入可以防止这种疾病的潜在负面后果。在许多视觉任务中，深度卷积神经网络（CNN）的表现要比人类表现更好，例如从视觉数据中进行医学诊断。这项研究的目的是评估深CNN从手写中诊断出特定学习障碍的学生的能力。通过应用转移学习，使用MobileNetv2深CNN体系结构。该模型是使用有497张手写样品图像的数据集对特定学习障碍诊断的学生以及没有这种诊断的数据集进行了训练的。在验证上产生的特定学习障碍的检测设置了接收器工作特性曲线下的平均面积为0.89。这是一种新颖的尝试，旨在检测使用深度学习诊断特定学习障碍的学生。为这项研究构建的系统，可能会对可能符合特定学习障碍标准的学生进行快速初步筛查。
Early detection is key for treating those diagnosed with specific learning disorder, which includes problems with spelling, grammar, punctuation, clarity and organization of written expression. Intervening early can prevent potential negative consequences from this disorder. Deep convolutional neural networks (CNNs) perform better than human beings in many visual tasks such as making a medical diagnosis from visual data. The purpose of this study was to evaluate the ability of a deep CNN to detect students with a diagnosis of specific learning disorder from their handwriting. The MobileNetV2 deep CNN architecture was used by applying transfer learning. The model was trained using a data set of 497 images of handwriting samples from students with a diagnosis of specific learning disorder, as well as those without this diagnosis. The detection of a specific learning disorder yielded on the validation set a mean area under the receiver operating characteristics curve of 0.89. This is a novel attempt to detect students with the diagnosis of specific learning disorder using deep learning. Such a system as was built for this study, may potentially provide fast initial screening of students who may meet the criteria for a diagnosis of specific learning disorder.
