Autism Spectrum Disorder Detection Using a Weighted-Average Ensemble of Deep Convolutional Neural Networks on Eye-Tracking Images

Masroor Ahmed, Sadam Hussain, Ivan Amaya, José Carlos Ortiz-Bayliss

Autism Spectrum Disorder is a long-term neurodevelopmental disorder. Early diagnosis is crucial for timely rehabilitation and intervention. Recently, machine learning and deep learning techniques have been widely explored and have produced encouraging results using eye-tracking scanpath images for the early detection of ASD. However, these approaches exhibit inconsistent performance and classification error rates, as well as limited generalization, due to differences in learning approaches and architectural designs across individual models. To address these problems, we employed a weighted-average ensemble of deep learning models using eye-tracking scanpath images. In this work, two different pretrained convolutional neural networks were selected, including Xception and VGG16, based on their proven efficacy and performance. Moreover, we fine-tuned each model individually and evaluated them on a dataset containing eye-tracking scanpath images. We implemented a weighted-average ensemble technique to combine the diverse predictions of the two models. It reduces classification errors and improves the model’s generalization and overall performance. The adopted weighted-average ensemble technique achieved an accuracy of 98.18%, with a perfect recall, and a competitive Area Under the Curve (AUC) of 99.59%. These findings highlight that applying a weighted average to integrate multiple models’ predictions strengthens the generalization and reliability of ASD detection.