Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental disorder in which the neurology of an autistic person is severely hampered. A child with autism has difficulty answering his name, avoids eye contact, and cannot express his emotions. Early diagnosis can assist children with ASD enhance their intellectual abilities while reducing autistic symptoms. In computer vision, determining developmental disorder problems from facial image data is a significant but largely unexplored challenge. This paper proposed a method to classify autistic and non-autistic facial images using model 1 (Xception) and model 2 (Augmentation + Xception). Among Model 1 and Model 2, Model 2 achieved higher accuracy of 98% and a minimum loss of 0.08.
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Acknowledgment
The authors are grateful to the Ministry of Education, Govt. of India and Indian Institute of Information Technology, Allahabad for providing all resources and financial support to complete this work.
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Shrivastava, T., Singh, V., Agrawal, A. (2023). Autism Spectrum Disorder Classification of Facial Images Using Xception Model and Transfer Learning with Image Augmentation. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds) Neural Information Processing. ICONIP 2022. Lecture Notes in Computer Science, vol 13625. Springer, Cham. https://doi.org/10.1007/978-3-031-30111-7_15
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