Diagnosis of Autism in Children using Facial Analysis and Deep Learning

TL;DR

This paper presents a deep learning approach using facial analysis to diagnose autism in children with high accuracy, enabling a cost-effective and efficient diagnostic method based solely on images.

Contribution

The study introduces a novel deep learning model combining facial analysis and autism classification, achieving 94.6% accuracy with a simple image-based approach.

Findings

Achieved 94.6% classification accuracy.

Used 3,014 images with balanced classes.

Proposed a cost-effective, image-only diagnostic method.

Abstract

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.