Diabetes Prediction using Machine Learning Algorithms

Abstract

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 VGG-16, spatial pyramid pooling layer (SPP) is stacked to make a highly nonlinear scale-invariant deep model called the VGG-16 model. The proposed VGG-16 model can process a DR image at any scale due to the SPP layer’s virtue. Moreover, the stacking adds extra nonlinearity to the model and tends to better classification. The experimental results show that the proposed model performs better