Optimizing diabetic retinopathy detection with inception-V4 and dynamic version of snow leopard optimization algorithm

•Integration of an optimized Inception-V4 for Diabetic Retinopathy Detection.•Using dynamic version of Snow Leopard Optimization algorithm for optimizing Inception-V4.•Improved diagnostic performance and early-stage detection.•Comparative evaluation with state-of-the-art models. Diabetic retinopathy...

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Bibliographic Details
Published in:Biomedical signal processing and control Vol. 96; p. 106501
Main Authors: Yang, Jing, Qin, Haoshen, Por, Lip Yee, Shaikh, Zaffar Ahmed, Alfarraj, Osama, Tolba, Amr, Elghatwary, Magdy, Thwin, Myo
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.10.2024
Subjects:
Diabetic retinopathy
Dynamic snow leopard optimization
Early detection
Inception-V4
Medical imaging
Diabetic retinopathy
Dynamic snow leopard optimization
Medical imaging
Inception-V4
Early detection
ISSN:1746-8094
Online Access:Get full text
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Summary:•Integration of an optimized Inception-V4 for Diabetic Retinopathy Detection.•Using dynamic version of Snow Leopard Optimization algorithm for optimizing Inception-V4.•Improved diagnostic performance and early-stage detection.•Comparative evaluation with state-of-the-art models. 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.
ISSN:1746-8094
DOI:10.1016/j.bspc.2024.106501