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. 2022 Jan 20:15:789998.
doi: 10.3389/fncom.2021.789998. eCollection 2021.

Deep Learning for Autism Diagnosis and Facial Analysis in Children

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

Deep Learning for Autism Diagnosis and Facial Analysis in Children

Mohammad-Parsa Hosseini et al. Front Comput Neurosci. .

Retraction in

Abstract

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. Patients with autism struggle with social skills, repetitive behaviors, and communication, both verbal and non-verbal. 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 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.

Keywords: autism; children; deep learning; diagnosis; facial image analysis.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
On the (Left) is a child with autism, and on the (Right) is a child without autism, in order to compare some facial features.
Figure 2
Figure 2
Some images used in the deep learning training step. (Top) Children who have autism. (Bottom) Children who do not have autism.
Figure 3
Figure 3
The algorithm architecture of the proposed model, illustrating the use of MobileNet, followed by two dense layers to perform image recognition. MobileNet uses CNN to predict what is the shape of the object present and what is matched with it from the images.
Figure 4
Figure 4
By adding one epoch at a time, this figure shows how the loss changes.
Figure 5
Figure 5
With the addition of one epoch, this figure shows how accuracy changed for training, validation, and testing data.
See this image and copyright information in PMC

References

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