Auto-ASD Detector: Exploiting Computational Intelligence for autism spectrum disorders Detection in Children via Facial Analysis

Abstract

Asperger's syndrome, difficulties with disintegration in children, and autism are all included in the category of complex neurodevelopmental diseases known as autism spectrum disorders (ASD). Individuals who are autistic struggle greatly to keep up with society's speed, have poor communication skills, and struggle to express their emotions in the right ways. Early diagnosis and intervention can greatly improve the long-term outcomes for children with ASD. Several studies have identified key characteristics of autism using a variety of methods, including feature extraction, eye tracking, and speech recognition. As opposed to a person's emotional condition, facial recognition is more crucial in identifying autism. Early diagnosis and intervention can greatly improve the long-term outcomes for children with ASD. Hence, cutting-edge information technology that employs artificial intelligence (AI) techniques has assisted in the early diagnosis of ASD based on face pattern recognition. Among these techniques are deep learning (DL) have been utilized or suggested for detecting autism in youngsters. Herein, we applied a technique for accurate autism detection in children using facial analysis with the aid of computational intelligence. The proposed approach involves analyzing facial features and expressions to identify patterns which are associated with ASD. This is achieved by leveraging application of convolutional neural network (CNNs) to extract meaningful features from facial images. The extracted features are used to accurately classify children as either having or not having ASD. To evaluate the proposed approach, a dataset of facial images of children with and without ASD is used to train and validate the proposed technique. Also, to assess their performance in accurately detecting ASD. The proposed technique has the potential to revolutionize the way ASD is diagnosed by providing an objective and reliable tool for early detection and intervention.