Particle Swarm Optimization of Convolutional Neural Networks for Diabetic Retinopathy Classification

This work proposes convolutional neural networks (CNNs) and particle swarm optimization (PSO) for diabetic retinopathy classification. Particle swarm optimization seeks to minimize the classification error, designing the convolutional neural network using different preprocessing methods to compare results. The parameters optimized to design the CNN are the number of convolutional layers, filters with their filters size, pool size, algorithm for the learning process, number of fully connecter layers with their number of neurons, batch size, and finally the number of epochs. Among the preprocessing applied are: extraction of the retina and applying a histogram equalization to the red, green, and blue channels. The database used to test the proposed method is APTOS 2019, where the best result achieved is 96.59%, with an average of 95.33%.