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Alzheimer's Disease (AD) causes a continuous decline in memory, thinking, andjudgment. Traditional diagnoses are usually based on clinical experience, whichis limited by some realistic factors. In this paper, we focus on exploitingdeep learning techniques to diagnose AD based on eye-tracking behaviors. Visualattention, as typical eye-tracking behavior, is of great clinical value todetect cognitive abnormalities in AD patients. To better analyze thedifferences in visual attention between AD patients and normals, we firstconduct a 3D comprehensive visual task on a non-invasive eye-tracking system tocollect visual attention heatmaps. We then propose a multi-layered comparisonconvolution neural network (MC-CNN) to distinguish the visual attentiondifferences between AD patients and normals. In MC-CNN, the multi-layeredrepresentations of heatmaps are obtained by hierarchical convolution to betterencode eye-movement behaviors, which are further integrated into a distancevector to benefit the comprehensive visual task. Extensive experimental resultson the collected dataset demonstrate that MC-CNN achieves consistent validityin classifying AD patients and normals with eye-tracking data.

Deep Learning-based Eye-Tracking Analysis for Diagnosis of Alzheimer's Disease Using 3D Comprehensive Visual Stimuli