  In this paper, we introduce a deep learning model to classify children as
either healthy or potentially autistic with 94.6% accuracy using Deep Learning.
Autistic patients struggle with social skills, repetitive behaviors, and
communication, both verbal and nonverbal. Although the disease is considered to
be genetic, the highest rates of accurate diagnosis occur when the child is
tested on behavioral characteristics and facial features. Patients have a
common pattern of distinct facial deformities, allowing researchers to analyze
only an image of the child to determine if the child has the disease. While
there are other techniques and models used for facial analysis and autism
classification on their own, our proposal bridges these two ideas allowing
classification in a cheaper, more efficient method. Our deep learning model
uses MobileNet and two dense layers in order to perform feature extraction and
image classification. The model is trained and tested using 3,014 images,
evenly split between children with autism and children without it. 90% of the
data is used for training, and 10% is used for testing. Based on our accuracy,
we propose that the diagnosis of autism can be done effectively using only a
picture. Additionally, there may be other diseases that are similarly
diagnosable.
