Automated Alzheimer's, Mild Cognitive Impairment, and Normal Aging Screening using Polar Transformation of Optic Disc...

Detecting Mild Cognitive Impairment (MCI) is crucial for mitigating the risk of Alzheimer's disease (AD), a leading global cause of death. However, the current gold standard for AD and MCI detection relies on specialized equipment often limited to large testing centers, particularly in low-resource settings like Thailand. Our previous work aimed to create a cost-effective MCI and AD screening method using fundus images but struggled to differentiate between AD and MCI. Henceforth, we developed the proposed methodology, utilizing DenseNet-121 on polar-transformed and zone-selected fundus images, which significantly enhances AD and MCI classification, achieving 83% accuracy, 90% sensitivity, 77% specificity, 87% precision, and an F-1 score of 88%. Moreover, the model's Grad-Cam++ heatmap highlights vasculature differences, particularly in tortuosity and thickness, between AD and MCI fundus images. Combined with our previous work, we created a fully automated pipeline model for MCI, AD, and Normal aging classification, which is inexpensive, fast, and non-invasive with an overall 3-class accuracy of 88%.