论文标题
使用面部分析和深度学习诊断儿童自闭症
Diagnosis of Autism in Children using Facial Analysis and Deep Learning
论文作者
论文摘要
在本文中，我们介绍了一种深度学习模型，将儿童归类为健康或潜在的自闭症，使用深度学习的精度为94.6％。自闭症患者在社交技能，重复行为以及言语和非语言方面挣扎。尽管该疾病被认为是遗传的，但是当对儿童进行行为特征和面部特征测试时，准确诊断的率最高。患者的面部畸形有共同的模式，使研究人员只能分析儿童的形象，以确定孩子是否患有疾病。尽管还有其他用于面部分析和自闭症分类的技术和模型，但我们的建议桥梁桥接了这两个想法，允许以更便宜，更有效的方法进行分类。我们的深度学习模型使用Mobilenet和两个密集的层来执行特征提取和图像分类。使用3,014张图像对该模型进行训练和测试，并在自闭症儿童和没有该模型的儿童之间均匀分配。 90％的数据用于培训，10％用于测试。根据我们的准确性，我们建议只使用图片有效地进行自闭症的诊断。此外，可能还有其他可以诊断出的疾病。
In this paper, we introduce a deep learning model to classify children as either healthy or potentially autistic with 94.6% accuracy using Deep Learning. Autistic patients struggle with social skills, repetitive behaviors, and communication, both verbal and nonverbal. Although the disease is considered to be genetic, the highest rates of accurate diagnosis occur when the child is tested on behavioral characteristics and facial features. Patients have a common pattern of distinct facial deformities, allowing researchers to analyze only an image of the child to determine if the child has the disease. While there are other techniques and models used for facial analysis and autism classification on their own, our proposal bridges these two ideas allowing classification in a cheaper, more efficient method. Our deep learning model uses MobileNet and two dense layers in order to perform feature extraction and image classification. The model is trained and tested using 3,014 images, evenly split between children with autism and children without it. 90% of the data is used for training, and 10% is used for testing. Based on our accuracy, we propose that the diagnosis of autism can be done effectively using only a picture. Additionally, there may be other diseases that are similarly diagnosable.
