An experiment was performed to investigate an alternative pooling method - Variable Stride (VS) - for use in convolutional neural networks. Three VS methods were compared to Maxpool and Avgpool in three different network configurations tasked with classifying diabetic retinopathy images between healthy retinas and retinas with advanced retinopathy. Each combination of network structure and pooling method was run multiple times, and the AUCs, losses, accuracies, as well as the training speed of each run were all collected. Two-tailed t-tests were then run on the prior metrics to quantify the relative performance of each pooling method in each scenario. It was found that VS consistently performs worse than Maxpool and Avgpool in this specific scenario across all metrics.