Paper Title: DETECTION OF AUTISM SPECTRUM DISORDER USING DEEP LEARNING

Author's name: B.Sivadharshini, Dr. P D Sheba Kezia Malarchelvi

A neurodevelopmental condition known as autism spectrum disorder (ASD) impacts social interaction, behaviour, and cognitive functioning. ASD diagnosis can be challenging and time-consuming, but early identification and intervention can improve long-term outcomes. Autism spectrum disorder begins in early childhood and eventually causes problems functioning in society — socially, in school and at work, for example. Often children show symptoms of autism within the first year. Some children show signs of autism spectrum disorder in early infancy, such as reduced eye contact, lack of response to their name or indifference to caregivers. In this project. Deep Learning algorithm such as LSTM to determine the presence of disorder at an early stage. The datasets have been collected using Self-Stimulatory Behaviours Dataset (SSBD) and video dataset is used for designing the system. Blaze Pose algorithm is used for feature extraction. The datasets will be trained using deep learning algorithm and the model file has been generated. When an input image is given for disease prediction, as a result its severity will be calculated. The proposed system provides an effective solution to predict the presence of autism spectrum disorder in a more efficient way using the activity of the children.

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