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.
A Hybrid Deep Learning Framework for Emotion Recognition in Children with Autism During NAO Robot-Mediated Interaction
Department of Information Technology, School of Engineering, Cochin University of Science and Technology（科钦大学科学与技术学院信息科技系）；Indian Institute of Technology Kanpur（印度理工学院坎普尔学院）
Screening Autism Spectrum Disorder in children using Deep Learning Approach : Evaluating the classification model of YOLOv26s by comparing with other models
Coventry University（考文垂大学）；University of Memphis（孟菲斯大学）；University of Massachusetts Boston（马萨诸塞大学波士顿分校）；University of Nevada Las vegas（内华达大学拉斯维加斯分校）；Softwarica College（索夫瓦里卡学院）；GEICO（盖可保险公司）；Growphase Technology（格罗法斯科技）；University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；Curry College（库里学院）
Towards Child-Inclusive Clinical Video Understanding for Autism Spectrum Disorder
University of Southern California（南加州大学）；Korea University（高丽大学）；Boston University（波士顿大学）；University of California San Francisco（加州大学旧金山分校）；University of California Los Angeles（加州大学洛杉矶分校）
Detecting Children with Autism Spectrum Disorder based on Script-Centric Behavior Understanding with Emotional Enhancement
School of Computer Science, Wuhan University（武汉大学计算机学院）；Digital Innovation Research Center, Duke Kunshan University（杜克昆山大学数字创新研究中心）；School of Electronics and Information Technology, Sun Yat-sen University（中山大学电子与信息学院）；Child Development and Behavior Center, Third Affiliated Hospital of Sun Yat-sen University（中山大学第三附属医院儿童发展与行为中心）
Autism Spectrum Disorder Classification with Interpretability in Children based on Structural MRI Features Extracted using Contrastive Variational Autoencoder
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Shenzhen Key Laboratory of Intelligent Bioinformatics（深圳市智能生物信息学重点实验室）；Center for High Performance Computing, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院高性能计算中心）
A multimodal large language model for evidence-based autism spectrum disorder screening
Zhejiang Normal University（浙江师范大学）；State Grid Corporation of China（国家电网有限公司）；Huzhou Normal University（湖州师范学院）；Zhejiang University of Technology（浙江工业大学）
Hugging Rain Man: A Novel Facial Action Units Dataset for Analyzing Atypical Facial Expressions in Children with Autism Spectrum Disorder
Faculty of Artificial Intelligence in Education, Central China Normal University（华中师范大学教育人工智能学院）；Computer Science and Artificial Intelligence School, Wuhan University of Technology（武汉理工大学计算机科学与人工智能学院）
Beyond FACS: Data-driven Facial Expression Dictionaries, with Application to Predicting Autism
Center for Autism Research, The Children’s Hospital of Philadelphia（儿童医院费城医院自闭症研究中心）；Department of Psychiatry, University of Pennsylvania（宾夕法尼亚大学精神病学系）；Deliberate AI
AEGIS: A real-time multimodal augmented reality computer vision based system to assist facial expression recognition for individuals with autism spectrum disorder
University of Waterloo（滑铁卢大学）；Vision and Image Processing Lab（视觉与图像处理实验室）
Adults as Augmentations for Children in Facial Emotion Recognition with Contrastive Learning
Centrum Wiskunde & Informatica（荷兰数学与计算机科学研究中心）；University of Trieste（的里雅斯特大学）；Leiden University Medical Center（莱顿大学医学中心）；Delft University of Technology（代尔夫特理工大学）
A Deep Learning Approach for Multi-View Engagement Estimation of Children in a Child-Robot Joint Attention task
Athena Research and Innovation Center（雅典娜研究与创新中心）；National Technical Univ. of Athens（雅典国立技术大学）；Sorbonne Université（索邦大学）；CNRS（法国国家科学研究中心）；Institute of Intelligent Systems and Robotics（智能系统与机器人研究所）
UniAR: A Unified Framework for Autism Recognition Enhanced by Multi-View Prompt Learning
Wuhan University（武汉大学）；Northeast Normal University（东北师范大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Nanjing Agricultural University（南京农业大学）；Imperial College London（帝国理工学院）；The Hong Kong University of Science and Technology（香港科技大学）；Harvard University（哈佛大学）
Early Autism Diagnosis based on Path Signature and Siamese Unsupervised Feature Compressor
University of Pennsylvania（宾夕法尼亚大学）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；The Affiliated Shenzhen School of Guangdong Experimental High School（广东实验中学附属深圳学校）；Pazhou Lab（琶洲实验室）
Advanced Gesture Recognition for Autism Spectrum Disorder Detection: Integrating YOLOv7, Video Augmentation, and VideoMAE for Naturalistic Video Analysis
Indian Institute of Information Technology, Allahabad（阿拉哈巴德印度信息技术学院）
Autism Disease Detection Using Transfer Learning Techniques: Performance Comparison Between Central Processing Unit vs Graphics Processing Unit Functions for Neural Networks
Kennesaw State University（肯尼索州立大学）；University of Calabria（卡拉布里亚大学）
本文针对自闭症图像分类任务，采用多种迁移学习神经网络模型比较 CPU 与 GPU 的性能，结果表明 GPU 在执行时间和准确率上均优于 CPU。
Hybrid Vision Transformer-Mamba Framework for Autism Diagnosis via Eye-Tracking Analysis
Faculty of Science and Technology, Tissemsilt University（科技与技术学院，蒂塞姆西尔大学）；College of Engineering and Information Technology University of Dubai（工程与信息科技学院，迪拜大学）；College of Medicine and Health Sciences, Mohammed Bin Rashid University Dubai, UAE（医学与健康科学学院，迪拜穆罕默德·本·拉希德大学）；School of Clinical Medicine University of New South Wales, Australia（临床医学学院，新南威尔士大学）
Unlocking Multi-Site Clinical Data: A Federated Approach to Privacy-First Child Autism Behavior Analysis
Institute of Artificial Intelligence, University of Central Florida（中佛罗里达大学人工智能研究所）；Department of Clinical Sciences, College of Medicine, University of Central Florida（中佛罗里达大学医学院临床科学系）；Department of Computer Science, University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校计算机科学系）；Department of Computer Science, Northern Illinois University（北伊利诺伊大学计算机科学系）；Industrial and Systems Engineering, Northern Illinois University（北伊利诺伊大学工业与系统工程系）
Deep Learning Based Approach to Enhanced Recognition of Emotions and Behavioral Patterns of Autistic Children
Department of Information Technology（信息科技系）；Sri Lanka Institute of Information Technology（斯里兰卡信息科技研究所）；Department of Computer Systems Engineering（计算机系统工程系）
Enhancing Autism Spectrum Disorder Early Detection with the Parent-Child Dyads Block-Play Protocol and an Attention-enhanced GCN-xLSTM Hybrid Deep Learning Framework
Shandong University（山东大学）；University of Michigan（密歇根大学）；Qilu Hospital of Shandong University（山东大学齐鲁医院）
