Optimizing a Convolutional Neural Network with a Hierarchical Genetic Algorithm for Diabetic Retinopathy Detection

One of the worse conditions caused by diabetes mellitus (DM) is diabetic retinopathy (DR) and it can be irreversible if it is not treated in time. The patient with this condition can be completely blind because DR does not have symptoms until advanced stages. Because of this, some authors have been searching for a solution to an early detection of DR. One of the most used technologies for the detection of DR is the neural networks called: Convolutional neural networks (CNN). But design a CNN model from beginning could be slow. Along this work, we proposed the design of a hierarchical genetic algorithm (HGA) to find the best hyperparameters for a CNN model for the detection of DR. Before designing the hierarchical genetic algorithm, we applied pre-processing to the APTOS 2019 database. Then we executed 30 times the hierarchical genetic algorithm and achieved 0.9650 of accuracy mean and 0.007665 of standard deviation. The best CNN model got an accuracy of 0.9781 for DR detection.