POWER OF ALIGNMENT: EXPLORING THE EFFECT OF FACE ALIGNMENT ON ASD DIAGNOSIS USING FACIAL IMAGES

Abstract

Autism Spectrum Disorder (ASD) is a developmental disorder that impacts social communication and conduct. ASD lacks standard treatment protocols or medication, thus early identification and proper intervention are the most effective procedures to treat this disorder. Artificial intelligence could be a very effective tool to be used in ASD diagnosis as this is free from human bias. This research examines the effect of face alignment for the early diagnosis of Autism Spectrum Disorder (ASD) using facial images with the possibility that face alignment can improve the prediction accuracy of deep learning algorithms. This work uses the SOTA deep learning-based face alignment algorithm MTCNN to preprocess the raw data. In addition, the impacts of facial alignment on ASD diagnosis using facial images are investigated using state-of-the-art CNN backbones such as ResNet50, Xception, and MobileNet. ResNet50V2 achieves the maximum prediction accuracy of 93.97% and AUC of 96.33% with the alignment of training samples, which is a substantial improvement over previous research. This research paves the way for a data-centric approach that can be applied to medical datasets in order to improve the efficacy of deep neural network algorithms used to develop smart medical devices for the benefit of mankind.