Dementia Detection via Retinal Hyperspectral Imaging and Deep Learning: Clinical Dataset Analysis and Comparative Evaluation of Multiple Architectures.

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

SUBMITTER: Lin WS 

PROVIDER: S-EPMC12730061 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature