Hierarchical genetic optimization of convolutional neural models for diabetic retinopathy classification

Abstract

Diabetic retinopathy (DR) is one of the worse conditions caused by diabetes mellitus (DM). DR can leave the patient completely blind because it may have no symptoms in its initial stages. Expert physicians have been developing technologies for early detection and classification of DR to prevent the increasing number of patients. Some authors have used convolutional neural networks for this purpose. Pre-processing methods for database are important to increase the accuracy detection of CNN, and the use for an optimization algorithm can further increase that accuracy. In this work, four pre-processing methods are presented to compare them and select the best one. Then the use of a hierarchical genetic algorithm (HGA) with the pre-processing method is done with the intention of increasing the classification accuracy of a new CNN model. Using the HGA increases the accuracies obtained by the pre-processing methods and outperforms the results obtained by other authors. In the binary study case (detection of DR) a 0.9781 in the highest accuracy was achieved, a 0.9650 in mean accuracy and 0.007665 in standard deviation. In the multi-class study case (classification of DR) a 0.7762 in the highest accuracy, 0.7596 in mean accuracy and 0.009948 in standard deviation.