Abstract
This study aimed to detect dementia using intelligent hyperspectral imaging (HSI), which enables the extraction of detailed spectral information from retinal tissues. A total of 3256 ophthalmoscopic images collected from 137 participants were analyzed. The spectral signatures of selected retinal regions were reconstructed using hyperspectral conversion techniques to examine wavelength-dependent variations associated with dementia. To assess the diagnostic capability of deep learning models, four convolutional neural network (CNN) architectures-ResNet50, Inception_v3, GoogLeNet, and EfficientNet-were implemented and benchmarked on two datasets: original ophthalmoscopic images (ORIs) and hyperspectral images (HSIs). The HSI-based models consistently demonstrated superior accuracy, achieving 84% with ResNet50, 83% with GoogLeNet, and 82% with EfficientNet, compared with 80-81% obtained from ORIs. Inception_v3 maintained an accuracy of 80% across both datasets. These results confirm that integrating spectral information enhances model sensitivity to dementia-related retinal changes, highlighting the potential of HSI for early and noninvasive detection.