Optimizing diabetic retinopathy detection with inception-V4 and dynamic version of snow leopard optimization algorithm
Document Type
Article
Publication Date
10-1-2024
Abstract
Diabetic retinopathy is a severe ocular condition that can result in vision loss due to damage to the retinal vessels. Early detection is of paramount importance in reducing the risk of further vision impairment and guiding appropriate treatment strategies. This study presents an innovative approach to enhance the accuracy and efficiency of diabetic retinopathy detection by integrating the Inception-V4 deep learning-based neural network with a modified dynamic Snow Leopard Optimization (DSLO) algorithm. The DSLO algorithm optimizes feature selection, thereby contributing to improved diagnostic performance. By analyzing digital images obtained during routine eye exams, automated image processing algorithms can identify early signs of diabetic retinopathy, such as leaking vessels or optic nerve edema. The proposed Inception-V4/DSLO model is evaluated using a practical dataset, Diabetic Retinopathy 2015, and compared to other state-of-the-art models, including mining local and long-range dependence (MLLD), parallel convolutional neural network (PCNN) and ELM classifier (PCNN/ELM), diabetic retinopathy using convolutional neural networks for feature extraction and classification (DRFEC), Retrained AlexNet convolutional neural network (R-AlexNet), and Deep-DR demonstrating superior performance and improved detection of early-stage diabetic retinopathy cases.
Keywords
Diabetic retinopathy, Inception-V4, Dynamic snow leopard optimization, Medical imaging, Early detection
Divisions
Computer
Funders
King Saud University (RSPD2024R681)
Publication Title
Biomedical Signal Processing and Control
Volume
96
Issue
A
Publisher
Elsevier
Publisher Location
125 London Wall, London, ENGLAND