The training, testing, and validation subcategories are further split into autistic and non-autistic folders. The validation component will be important for determining the quality of the model we use, which means we do not have to strictly rely on the accuracy of the model to determine its quality. If we decide to create our own machine learning model, the provided split of training, testing, and validating subgroups will be useful.

When downloaded, the data is provided in two ways: split into training, testing, and validation vs. This dataset is slightly unusual, as the publisher only had access to websites to gather all the images. In this work, we have used a dataset found on Kaggle, which consists of over three thousand images of both children with and without autism. Because of this, conducting facial recognition binary classification on images of children with autism and children who are labeled as healthy could allow us to diagnose the disease earlier and in a cheaper way. Figure 1 shows some of these differences.

They also have a shorter middle region of the face, including the cheeks and nose. The study found that children with autism have an unusually broad upper face, including wide-set eyes. Scientists at the University of Missouri found that children diagnosed with autism share common facial feature distinctions from children who are not diagnosed with the disease ( Aldridge et al., 2011 CBS News, 2017). We believe that facial recognition is the best possible way to diagnose a patient because of their distinct attributes. Interestingly, while ASD is believed to be a genetic disorder, it is mainly diagnosed through behavioral attributes: “the ways in which children diagnosed with ASD think, learn, and problem-solve can range from highly skilled to severely challenged.” Early detection and diagnosis are crucial for any patient with ASD as this may significantly help them with their disorder. A child can be diagnosed with ASD as early as 18 months old. Because there are so many forms of autism, it is technically called autism spectrum disorder (ASD) ( Austimspeaks, 2019). In 2018, the Centers for Disease Control and Prevention (CDC) claimed that about 1 in 59 children will be diagnosed with some form of autism. Additionally, there may be other diseases that are similarly diagnosable.Īutism is primarily a genetic disorder, though there are some environmental factors, that cause challenges with social skills, repetitive behaviors, speech, and non-verbal communication ( Alexander et al., 2007). Based on our accuracy, we propose that the diagnosis of autism can be done effectively using only a picture. 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. Our deep learning model uses MobileNet and two dense layers to perform feature extraction and image classification. 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. 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. 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 with autism struggle with social skills, repetitive behaviors, and communication, both verbal and non-verbal. In this paper, we introduce a deep learning model to classify children as either healthy or potentially having autism with 94.6% accuracy using Deep Learning. 3CIPCE, School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran.2AI Research, San Jose, CA, United States.

1Department of Bioengineering, Santa Clara University, Santa Clara, CA, United States.

Mohammad-Parsa Hosseini 1,2 Madison Beary 1 Alex Hadsell 1 Ryan Messersmith 1 Hamid Soltanian-Zadeh 3 *

Autism spectrum disorder tests