Diabetic Retinopathy Classification With Convolutional Neural Networks Designed by the Fuzzy Grey Wolf Optimizer With Parameter Adaptation

The detection and classification of diseases have become a field of interest for artificial intelligence in recent years, where the development of methods and models that allow support for specialists in different health fields has allowed early detection of diseases and the provision of timely treatment to patients. This work proposes the classification of diabetic retinopathy: binary and multi‐class. The first classification consists of detecting whether a patient has diabetic retinopathy, whereas the second classification seeks to detect and determine the level of the disease. The classification is performed using convolutional neural networks, which are optimized using a grey wolf optimizer algorithm. The optimizer has the objective of finding the architecture of the convolutional neural network, as well as the number of convolutional layers, filters, hidden layers, and neurons. The combination of this optimization algorithm with fuzzy logic allows dynamic adjustment of parameters based on current information. This work presents a comparison with other optimization methods such as genetic algorithms and particle swarm optimization. Optimization allows the convolutional neural network model to achieve a maximum accuracy of 0.979536176 for the detection study case and 0.980900407 by adding fuzzy logic, while a maximum accuracy of 0.780354679 is obtained for the classification study case and 0.76807642 by adding fuzzy logic. The results obtained show that the application of optimization methods and fuzzy logic allow the convolutional neural networks to have an advantage over other optimized architectures.