Grade classification of Alzheimer's disease using hyperspectral ophthalmoscope images

Abstract

Detecting Alzheimer’s disease presents considerable challenges in the medical field. Hyperspectral imaging offers a promising approach to identifying the disease by analyzing spectral information from the diseased retina. This study utilized 3,256 ophthalmoscopic images from 137 patients and applied hyperspectral imaging technology to capture spectral data from specific regions within these images. Deep learning models, including ResNet50, Inception v3, GoogLeNet, and EfficientNet, were introduced and evaluated for lesion detection accuracy. In the training phase, these artificial neural networks showed varying accuracies in lesion detection using both original and hyperspectral images. For instance, ResNet50 achieved 80% accuracy with original images and 84% with hyperspectral images, while Inception v3 consistently achieved 80% accuracy with both types of images. GoogLeNet demonstrated 81% accuracy with original images, improving to 83% with hyperspectral images, and EfficientNet recorded accuracies of 80% for original images and 82% for hyperspectral images. The combination of hyperspectral imaging and deep learning models shows promise in enhancing Alzheimer’s disease detection through ophthalmoscopic image analysis, with hyperspectral images exhibiting higher efficacy compared to original images.