Enhancing diabetic retinopathy detection through preprocessing and feature extraction with MGA-CSG algorithm

Abstract

Anticipatory monitoring of diabetic retinal (DR) disease is crucial in preventing vision loss and blindness, making it a leading cause of worldwide vision impairment. In this study, we propose a novel technique, the Modified Generative Adversarial-based Crossover Salp Grasshopper (MGA-CSG) approach, for early prediction and accurate classification of diabetic retinal diseases using fundus image datasets. The proposed MGA-CSG approach combines deep learning with an optimization algorithm to achieve superior classification accuracy. We start with pre-processing of data to attenuate image variation, convert intensities, denoise, and enhance contrast in the fundus images. Next, a Convolutional Neural Network (CNN) is used for feature extraction, capturing essential information from the images. To address the limited amount of feature vectors generated by the CNN and improve learning uncertainty, we employ a Generative Adversarial Network (GAN) model to augment the feature vectors. It generates additional images, allowing the classifier to train with fewer input samples, leading to improved classification performance. One of the key novelties of our approach lies in the utilization of both labeled and unlabeled data samples, enhancing the GAN training performance. We replace the binary classifier in the GAN with a multiclass classifier, enabling separate discrimination of retinal disease classes. The MGA-CSG approach leverages the crossover Grasshopper Optimizer Algorithm (GOA) and Salp Swarm Algorithm (SSA) to optimize the model and avoid falling into local optimal solutions. This optimization process further enhances the accuracy of diabetic retinal disease classification. Experimental results demonstrate the superiority of the proposed MGA-CSG approach, achieving high accuracy, precision, recall, specificity, F1-measure, and Kappa coefficient in predicting glaucoma, diabetic retinopathy, cataract, macular edema, and myopia. The classification achieved impressive accuracy, precision, recall, specificity, F1-measure, and Kappa coefficient of 98.8%, 98.7%, 96.5%, 97.1%, 98.2%, and 95%, respectively. Overall, our novel MGA-CSG approach offers an efficient and accurate method for early screening and classification of diabetic retinal diseases. The combination of CNN-based feature extraction, GAN-based augmentation, and MGA-CSG optimization contributes to the model's high performance. This approach holds significant potential in improving healthcare management and providing timely interventions for patients with retinal diseases.