Hyperparameter optimization of convolutional neural networks using particle swarm optimization for diabetic retinopathy detection

Abstract

The optimization of hyperparameters in convolutional neural networks (CNNs) is crucial for improving the accuracy and effectiveness of diabetic retinopathy (DR) image classification. Conventional approaches often rely on manual adjustments, which are time-consuming and inefficient. This research proposes variant particle swarm optimization (PSO) methods for fine-tuning the hyperparameters of pre-trained deep learning models-specifically PSO-VGG-16, PSO-DenseNet-121, and PSO-MobileNet. The proposed models enhance the exploratory capabilities of CNNs by searching for optimal hyperparameter configurations. Experiments were conducted using the APTOS 2019 DR image benchmark dataset. This study introduces a PSO-driven hyperparameter optimization framework tailored for pre-trained CNN architectures (VGG-16, DenseNet-121, and MobileNetV2) to improve DR classification performance. The proposed method outperforms conventional genetic algorithm (GA)-based tuning techniques, achieving superior accuracy and model robustness. The findings show that PSO achieved a classification accuracy of 97.41%, sensitivity of 96.8%, and specificity of 99.4%. Among the models, PSO-MobileNet demonstrated the best overall performance. These results confirm that PSO is a powerful tool for hyperparameter optimization, enhancing diagnostic accuracy and improving the efficiency of CNN models in medical image classification. This technique supports healthcare professionals in making timely and precise diagnoses, contributing to better patient outcomes and reduced strain on healthcare systems.