Diabetic retinopathy (DR) is a disease that damages retinal blood vessels and leads to blindness. Usually, colored fundus shots are used to diagnose this irreversible disease. The manual analysis (by clinicians) of the mentioned images is monotonous and error-prone. Hence, various computer vision hands-on engineering techniques are applied to predict the occurrences of the DR and its stages automatically. However, these methods are computationally expensive and lack to extract highly nonlinear features and, hence, fail to classify DR’s different stages effectively. This project focuses on classifying the DR’s different stages with the lowest possible learnable parameters to speed up the training and model convergence. The VGG16 and SVM are stacked to make a highly nonlinear scale-invariant deep model called the VGG16 model.