Control learning rate for autism facial detection via deep transfer learning

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder that affects social interaction and communication. Early detection of ASD can significantly improve outcomes for individuals with the disorder, and there has been increasing interest in using machine learning techniques to aid in the diagnosis of ASD. One promising approach is the use of deep learning techniques, particularly convolutional neural networks (CNNs), to classify facial images as indicative of ASD or not. However, choosing a learning rate for optimizing the performance of these deep CNNs can be tedious and may not always result in optimal convergence. In this paper, we propose a novel approach called the control subgradient algorithm (CSA) for tackling ASD diagnosis based on facial images using deep CNNs. CSA is a variation of the subgradient method in which the learning rate is updated by a control step in each iteration of each epoch. We apply CSA to the popular DensNet-121 CNN model and evaluate its performance on a publicly available facial ASD dataset. Our results show that CSA is faster than the baseline method and improves the classification accuracy and loss compared to the baseline. We also demonstrate the effectiveness of using CSA with [Formula: see text] -regularization to further improve the performance of our deep CNN model.