Early Childhood Autism Screening Through Facial Feature Extraction

Autism Spectrum Disorder (ASD) is a type of neurological disorder which affects the human’s communication skills, social skills, thinking skills etc. A person with autism tends to have social issues such as less interaction, less eye contact, less understanding, impaired language and issues with verbal and non verbal abilities. Autistic persons experience repetitive behaviour and often are hyper or hypo sensitive to external stimuli. This disorder is caused due to developmental changes in the structure of the brain. A person with autism will have different symptoms compared to other people with autism. It is mainly caused due to genetics, siblings with ASD, being born with low birth weight or having older parents. ASD can be cured in early stages in children if the right diagnosis is followed, such as conducting medical or neurological examinations, testing cognitive and language abilities of children, periodic or frequent observations such as blood tests and hearing tests. A child around or below 10 years can be detected easily with autism compared to adults. So, It is crucial to determine whether the child is having disorder or not at an early stage. Deep learning is one the most advancing areas in computer science, It solves the problems where Machine Learning fails. In this research, Deep Learning models, especially models based on Transfer Learning such as VGG16, InceptionV3, Efficient-Net-B0 and B7 were used to detect autism using facial images without any need of MRI or FMRI. The highest accuracy has been achieved around 85%.