Enhancing foveal avascular zone analysis for Alzheimer’s diagnosis with AI segmentation and machine learning using multiple radiomic features

Fivefold cross-validation results on the training set

We compared the diagnostic performance of the proposed technique and the baseline technique for AD diagnosis on the training set. We divided the training set into 85 OCTA scans, which were divided into five sets to apply fivefold cross-validation. Each diagnosis technique was trained five times, and the mean validation performance was considered as the final diagnostic performance. The training details for each technique are detailed below.

Training details

Training for baseline 1

As the CNN backbone used in baseline 1, we tested four representative models: ResNet³¹, DenseNet³², EfficientNet³³, and Inception³⁴. Each model was trained with fivefold cross-validation using the pretraining parameters on the ImageNet dataset for initialization. Each training procedure proceeded for 50 epochs by applying the cross-entropy loss⁴¹ to a…