Breakthroughs in the use of AI for Diabetic Retinopathy(DR) diagnosis, have made headway in making DR treatment more accessible but image and camera variability significantly affects the reproducibility of these machine learning algorithms. In an effort to improve the reproducibility of ML algorithms, we attempt to build a retinal image processing pipeline to quantify image quality taking into account luminance and blurriness, discarding poor quality images based on these metrics. Our pipeline further standardizes all images by cropping and resizing. To test the impact of our processing pipeline, we document the results of a 5-fold cross validation with and without the pipeline. Running images through the pipeline shows an increase in AUC performance attributable to an increase in image quality.