Diabetic retinopathy is an eye complication caused by a widespread disease named diabetes mellitus. The examination of retinal fundus images procured by retinography is the most commonly used method for diagnosing diabetic retinopathy. Strategies based on deep learning have shown promising results in detecting diabetic retinopathy, achieving performance similar to that of the human eye regarding image inspection. However, the performance of these strategies heavily depends on fine-tuning the algorithm hyper-parameters and big datasets. In this work, we propose training a Deep Learning model with Low-Rank Adaptation (LoRA) approach to classify three stages of Diabetic Retinopathy: i) no sign of diabetic retinopathy, ii) Non-proliferative diabetic retinopathy, and iii) proliferative diabetic retinopathy. We propose using a low-rank representation to reduce significantly the number of trainable parameters. The experiment shows that the LoRA approach for image classification of the three stages of diabetic retinopathy manages to obtain state-of-the-art results even with a small dataset.

Diabetic retinopathy is an eye complication of a widespread disease named diabetes mellitus. The most widely used method for diagnosing diabetic retinopathy is the analysis of retinal fundus images obtained by retinography. Deep Learning-based methods have shown promising results as a diagnostic tool for diabetic retinopathy, achieving, in some cases, performance close to the human inspection of images. However, the performance of these methods relies heavily on fine-tuning the algorithm hyperparameters and big data sets. In this work, we propose training a Deep Learning network with evolutionary algorithms to classify three stages of Diabetic Retinopathy: i) no sign of diabetic retinopathy, ii) Non-proliferative dia-betic retinopathy, and iii) proliferative diabetic retinopathy. We propose a neuroevolution methodology for selecting the most efficient Deep Learning model. The results of the neuroevolution methodology were improved by including Simulated Annealing strategies, Population Reinitialization, and ensembles. With high accuracy, sensitivity, specificity, and kappa index rates of 0.889, 0.889, 0.951, and 0.822, respectively, in the best case found, the experiments show that our neuroevolution methodology for selecting the Deep Learning model hyperparameters is a competitive alternative for training deep neural networks to classify three stages of diabetic retinopathy even with a small data set.