Retinal Fundus Image-Based Analysis of Diabetic Retinopathy Detection and Classification Model Using Improved Dung Beetle Optimization with Deep Transfer Learning Techniques

International Journal of Electronics and Communication Engineering

© 2025 by SSRG - IJECE Journal

Volume 12 Issue 6

Year of Publication : 2025

Authors : A. Kokila, R. Shankar, S. Duraisamy

10.14445/23488549/IJECE-V12I6P124

How to Cite?

A. Kokila, R. Shankar, S. Duraisamy, "Retinal Fundus Image-Based Analysis of Diabetic Retinopathy Detection and Classification Model Using Improved Dung Beetle Optimization with Deep Transfer Learning Techniques," SSRG International Journal of Electronics and Communication Engineering, vol. 12,  no. 6, pp. 304-314, 2025. Crossref, https://doi.org/10.14445/23488549/IJECE-V12I6P124

Abstract:

Diabetic Retinopathy (DR) is a microvascular difficulty of diabetes and a primary reason for vision loss in the developed or developing world. DR typically starts as initial microvascular modifications in the retinal blood vessels. For DR's recognition and development study, colour fundus images are the best approach for non-invasive eye fundus imaging. Recent automated techniques for DR grading combine the recognition of each symptom. In recent years, Deep Learning (DL) models were presented to automatically measure DR, a predominant vision-impairing disorder, helping ophthalmologists express individualized treatment approaches for patients. In this study, a Retinal Fundus Image-Based Analysis of Diabetic Retinopathy Detection Using Improved Dung Beetle Optimization and Deep Transfer Learning Techniques (RFIADRD-IDBODTLT) technique is proposed. The proposed RFIADRD-IDBODTLT technique relies on the advanced and automatic model for DR classification and grading on fundus images. Initially, image pre-processing is performed using the Sobel Filter (SF) model to remove noise in an input image dataset. Furthermore, the CapsNet model is employed for feature extraction. The Bidirectional Long Short-Term Memory (BiLSTM) model is utilized to classify DR. Finally, the hyperparameter selection of the BiLSTM model is performed by implementing the Improved Dung Beetle Optimization (IDBO) model. Experimentation is performed to validate the RFIADRD-IDBODTLT approach under the DR detection dataset. The performance validation of the RFIADRD-IDBODTLT approach demonstrated a superior accuracy value of 95.30% over existing models.

Keywords:

Diabetic Retinopathy, Retinal Fundus Image, Improved Dung Beetle Optimization, Deep Learning, Image Pre-Processing.

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