Abstract
Autism is a neurobehavioral problem that hinders to interact with others. Autistic Spectrum Disorder (ASD) is a psychological disorder that hampers procurement of etymological, communication, cognitive, social skills and Stereotypical motor behaviors and capabilities. Recent research revealing that Autism Spectrum Disorder can be diagnosed using gaze structures which has opened up a new field where visual focus modelling could be highly used. Diagnosis of ASD becomes a difficult task due to wide range of symptoms and severity of ASD. Deep neural networks have been widely employed and have shown to perform well in a variety of visual data processing applications. In this paper, typical developed (TD) or ASD is classified using Convolution neural Networks (CNN) for the fixation maps of the corresponding observer's gaze at a given image. The objective of this paper is to observe whether eye-tracking data of fixation map could classify children with ASD and typical development (TD). We further investigated whether features on visual fixation would attain better classification performance. The proposed CNN model achieves 75.23% accuracy for validation.
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