The Signal Processing and Applied Mathematics research group at the Nevada National Security Site (NNSS) is pleased to partner with students at Embry-Riddle Aeronautical University (ERAU) to study and develop a novel pooling method for convolutional neural networks (CNN), using a publicly available data set on diabetic retinopathy images. While most image data sets perform well under existing CNN architectures, there are still challenging datasets which require further advancements, prompting us to develop new techniques for CNNs in order to satisfactorily train a network. NNSS have developed a new pooling method called "variable stride" that demonstrates advantages for networks to train faster and more efficiently. The goal of this partnership is to provide feedback and evaluation of new techniques and studying the effectiveness of this method, compared to other pooling methods.