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Optimized automated detection of diabetic retinopathy severity: integrating improved multithresholding tunicate swarm algorithm and improved hybrid butterfly optimization

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Abstract

Diabetic retinopathy, a complication of diabetes, damages the retina due to prolonged high blood sugar levels, leading to vision impairment and blindness. Early detection through regular eye exams and proper diabetes management are crucial in preventing vision loss. DR is categorized into five classes based on severity, ranging from no retinopathy to proliferative diabetic retinopathy. This study proposes an automated detection method using fundus images. Image segmentation divides fundus images into homogeneous regions, facilitating feature extraction. Feature selection aims to reduce computational costs and improve classification accuracy by selecting relevant features. The proposed algorithm integrates an Improved Tunicate Swarm Algorithm (ITSA) with Renyi's entropy for enhanced adaptability in the initial and final stages. An Improved Hybrid Butterfly Optimization (IHBO) Algorithm is also introduced for feature selection. The effectiveness of the proposed method is demonstrated using retinal fundus image datasets, achieving promising results in DR severity classification. For the IDRiD dataset, the proposed model achieves a segmentation Dice coefficient of 98.06% and classification accuracy of 98.21%. In contrast, the E-Optha dataset attains a segmentation Dice coefficient of 97.95% and classification accuracy of 99.96%. Experimental results indicate the algorithm's ability to accurately classify DR severity levels, highlighting its potential for early detection and prevention of diabetes-related blindness.

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Data availability

Yes.

Abbreviations

RILBP-YNet:

Residual Inception Local Binary Pattern YNetwork

CLCNet:

Contextual and local collaborative network

DBUnet:

Dual Branch

NB:

Naïve Bayes

RF:

Random Forest

SVM:

Support vector machine

DT:

Decision tree

PSO:

Particle Swarm Optimization

ALO:

Ant Lion Optimization

WOA:

Whale Optimization

SGO:

Sea Gull Optimization

CSO:

Cuckoo Search optimization

TSO:

Tunicate Swarm Optimization

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Vijayawada, Andhra Pradesh, India

    Usharani Bhimavarapu

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Bhimavarapu, U. Optimized automated detection of diabetic retinopathy severity: integrating improved multithresholding tunicate swarm algorithm and improved hybrid butterfly optimization. Health Inf Sci Syst 12, 42 (2024). https://doi.org/10.1007/s13755-024-00301-x

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