Abstract:
The brain development and physical appearance of the face are both impacted by the neurologic disorder known as Autism
Spectrum Disorder (ASD). Children with ASD exhibit different facial landmarks from children who normally grow with Typical
Developing (TD), despite the fact that the disorder is thought to be inherited. When a child's behavioral traits and facial features are
examined, the likelihood of an accurate diagnosis is highest. In this work, we provide a deep learning model-based method for
diagnosing autism that divides children into two categories: possibly healthy and potentially unhealthy. The Tesorflow and Keras
libraries are used by the suggested deep learning model to carry out feature extraction and picture classification. A dataset obtained
from the Kaggle repository is used to train and evaluate the model. the dataset that was used to test this model consisted of 2,122 that
is excluded from the original 2,940 images due to the quality and race. The testing of the proposed model results in an Area Under
Curve (AUC) with 99.8% and accuracy of 97.3%. This model proves its high diagnosis accuracy, ease of use, and fast decision.