In this research, we propose a novel approach using deep learning models, specifically DenseNet-201, EfficientNet B7, ResNet-50, and MobileNetV2, to analyze eye-tracking scan paths for ASD detection.

Effective and accurate diagnosis of Autism Spectrum Disorder (ASD) has become crucial for early intervention and a better outcome for patients. This study attempts to improve ASD detection through ...

We analyze an eye-tracking dataset using enhanced image processing techniques to extract critical visual features. This study introduces a hybrid classification approach that integrates multiple deep ...

Early autism detection via eye-tracking tools offers objective assessments, allowing timely interventions that enhance developmental outcomes and access to care. Skip to main content Mobile Navigation ...

Alsharif, A., Khan, M.A. and Hossain, M.A. (2023) Automated Detection of Autism Spectrum Disorder Using Deep Learning and Eye-Tracking Data. IEEE Access, 11, 45678-45689.

The project follows a two-stage approach: Model Development: CNN models are developed for the image dataset using architectures like Inception, VGG-19, and MobileNet.For the eye-tracking dataset, LSTM ...