Abstract
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%).
Highlights
Autism spectrum disorders (ASD) refer to a group of complex neurodevelopmental disorders of the brain such as autism, childhood disintegrative disorders, and Asperger’s syndrome, which, as the term “spectrum” implies, have a wide range of symptoms and levels of severity [1]. ese disorders are currently included in the International Statistical Classification of Diseases and Related Health Problems under Mental and Behavioral Disorders, in the category of Pervasive Developmental Disorders [2]. e earliest symptoms of ASD often appear within the first year of life [3–6] and may include lack of eye contact, lack of response to name calling, and indifference to caregivers
People with autism face difficulties and challenges in understanding the world around them and in understanding their thoughts, feelings, and needs. e world surrounding a person with autism seems to him like a horror movie, and he finds some sounds and lights and even smells and tastes of foods frightening and sometimes painful. us, when a sudden change occurs in their world, they are terrified that no one else can understand
Three advanced deep learning models, namely, Xception, VGG19, and NASNetMobile were considered for use in diagnosing autism. e empirical results of these models were presented, and it was noted that the Xception model attained the highest accuracy of 91%
Summary
Autism spectrum disorders (ASD) refer to a group of complex neurodevelopmental disorders of the brain such as autism, childhood disintegrative disorders, and Asperger’s syndrome, which, as the term “spectrum” implies, have a wide range of symptoms and levels of severity [1]. ese disorders are currently included in the International Statistical Classification of Diseases and Related Health Problems under Mental and Behavioral Disorders, in the category of Pervasive Developmental Disorders [2]. e earliest symptoms of ASD often appear within the first year of life [3–6] and may include lack of eye contact, lack of response to name calling, and indifference to caregivers. Our study demonstrated the use of a well-trained classification model (based on transfer learning) to detect autism from an image of a child. (ii) e Xception model showed the best performance of the three pretrained deep learning algorithms (iii) A system was designed to help health officials to detect ASD through eye and face identification (iv) e developing system has been validated and examined using various methods. 2. Materials and Methods is study proposes a deep learning model based on transfer learning, namely, Xception, NASNETMobile, and VGG1 9 to detect autism using facial features of autistic and normal children. The normalization method was applied; the dataset was rescaling the parameters of all the images from the pixel values [0, 255] to [0, 1]
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