Classification and Detection of Autism Spectrum Disorder Based on Deep Learning Algorithms

• Published in: Computational intelligence and neuroscience Vol. 2022; pp. 1 - 10
• Main Authors: Alsaade, Fawaz Waselallah, Alzahrani, Mohammed Saeed
• Format: Journal Article
• Language: English
• Published: United States Hindawi 28.02.2022; John Wiley & Sons, Inc
• Subjects: Algorithms; Applications programs; Artificial intelligence; Artificial neural networks; Autism; Autism Spectrum Disorder - diagnosis; Autistic children; Behavior; Biomedical data; Biometry; Brain; Child; Children; Classification; Coronaviruses; COVID-19; Data mining; Datasets; Deep Learning; Digital media; Evaluation; Face; Face recognition; Facial recognition technology; Feature extraction; Humans; Machine learning; Medical imaging; Medical imaging equipment; Mental disorders; Mental illness; Nervous system diseases; Neural networks; Neural Networks, Computer; Neurological diseases; Pattern recognition; Social interactions; Social networks; Transfer learning
• ISSN: 1687-5265; 1687-5273; 1687-5273
• DOI: 10.1155/2022/8709145

Autism spectrum disorder (ASD) is a type of mental illness that can be detected by using social media data and biomedical images. Autism spectrum disorder (ASD) is a neurological disease correlated with brain growth that later impacts the physical impression of the face. Children with ASD have dissimilar facial landmarks, which set them noticeably apart from typically developed (TD) children. Novelty of the proposed research is to design a system that is based on autism spectrum disorder detection on social media and face recognition. To identify such landmarks, deep learning techniques may be used, but they require a precise technology for extracting and producing the proper patterns of the face features. This study assists communities and psychiatrists in experimentally detecting autism based on facial features, by using an uncomplicated web application based on a deep learning system, that is, a convolutional neural network with transfer learning and the flask framework. Xception, Visual Geometry Group Network (VGG19), and NASNETMobile are the pretrained models that were used for the classification task. The dataset that was used to test these models was collected from the Kaggle platform and consisted of 2,940 face images. Standard evaluation metrics such as accuracy, specificity, and sensitivity were used to evaluate the results of the three deep learning models. The Xception model achieved the highest accuracy result of 91%, followed by VGG19 (80%) and NASNETMobile (78%).