Akter Tania, Ali Mohammad Hanif, Khan Md Imran, Satu Md Shahriare, Uddin Md Jamal, Alyami Salem A, Ali Sarwar, Azad Akm, Moni Mohammad Ali
Department of Computer Science and Engineering, Jahangirnagar University, Savar, Dhaka 1342, Bangladesh.
Department of Computer Science and Engineering, Gono Bishwabidyalay, Savar, Dhaka 1344, Bangladesh.
Brain Sci. 2021 May 31;11(6):734. doi: 10.3390/brainsci11060734.
Autism spectrum disorder (ASD) is a complex neuro-developmental disorder that affects social skills, language, speech and communication. Early detection of ASD individuals, especially children, could help to devise and strategize right therapeutic plan at right time. Human faces encode important markers that can be used to identify ASD by analyzing facial features, eye contact, and so on. In this work, an improved transfer-learning-based autism face recognition framework is proposed to identify kids with ASD in the early stages more precisely. Therefore, we have collected face images of children with ASD from the Kaggle data repository, and various machine learning and deep learning classifiers and other transfer-learning-based pre-trained models were applied. We observed that our improved MobileNet-V1 model demonstrates the best accuracy of 90.67% and the lowest 9.33% value of both fall-out and miss rate compared to the other classifiers and pre-trained models. Furthermore, this classifier is used to identify different ASD groups investigating only autism image data using k-means clustering technique. Thus, the improved MobileNet-V1 model showed the highest accuracy (92.10%) for k = 2 autism sub-types. We hope this model will be useful for physicians to detect autistic children more explicitly at the early stage.
自闭症谱系障碍（ASD）是一种复杂的神经发育障碍，会影响社交技能、语言、言语和沟通能力。尽早发现ASD患者，尤其是儿童，有助于在恰当的时间制定并策划正确的治疗方案。人类面部蕴含重要特征，可通过分析面部特征、眼神交流等用于识别自闭症。在这项研究中，我们提出了一种改进的基于迁移学习的自闭症面部识别框架，以更精确地在早期阶段识别患有ASD的儿童。为此，我们从Kaggle数据存储库中收集了自闭症儿童的面部图像，并应用了各种机器学习和深度学习分类器以及其他基于迁移学习的预训练模型。我们观察到，与其他分类器和预训练模型相比，我们改进后的MobileNet-V1模型展现出了90.67%的最佳准确率，以及9.33%的最低误报率和漏报率。此外，该分类器还用于通过k均值聚类技术识别仅使用自闭症图像数据的不同ASD群体。因此，对于k = 2的自闭症子类型，改进后的MobileNet-V1模型显示出了最高的准确率（92.10%）。我们希望该模型能帮助医生在早期更准确地检测出自闭症儿童。
