AUTISM DETECTION USING RESNET50 & XCEPTION TRANSFER LEARNING

Abstract

Autism spectrum disorder (ASD) is a type of mental illness that can be detected by using social
media data and biomedical images. Autism spectrum disorder (ASD) is a neurological disease
correlated with brain growth that later impacts the physical impression of the face. Children
with ASD have dissimilar facial landmarks, which set them noticeably apart from typically
developed (TD) children. Novelty of the proposed research is to design a system that is based
on autism spectrum disorder detection on social media and face recognition. To identify such
landmarks, deep learning techniques may be used, but they require a precise technology for
extracting and producing the proper patterns of the face features. This study assists communities
and psychiatrists in experimentally detecting autism based on facial features, by using an
uncomplicated web application based on a deep learning system, that is, a convolutional neural
network with transfer learning and the flask framework. Xception, and Renet50 are the
pretrained models that were used for the classification task. The dataset that was used to test
these models was collected from the Kaggle platform and consisted of few face images.
Standard evaluation metrics such as accuracy, specificity, and sensitivity were used to evaluate
the results of the three deep learning models. The Xception model achieved the highest
accuracy result of 91% and Resnet50 model (98%).