Early detection of Alzheimer’s disease (AD) is crucial for effective intervention. Recent studies suggested that eye movements could serve as biomarkers for AD. This study analyzed eye movement data from visuospatial memory tasks and proposed a deep learning-based approach using Convolutional Neural Networks (CNNs) to classify AD patients based on gaze heatmaps. Instead of manual feature extraction, we used a three-channel input: the heatmap from viewing the original image, the heatmap from the manipulated image, and a region-highlighted image. This allowed the CNN to learn relevant patterns automatically. The model was trained on 24 participants (11 AD, 11 non-AD, 2 excluded) and achieved an AUC of 0.79, demonstrating moderate predictive performance.