Ensemble Diabetic Retinopathy Severity Classification Framework With Optimized VGG16, Resnet, and Inception Features

A. Jeba Sheela; M. Krishnamurthy

doi:10.9781/ijimai.2025.09.003

Authors

A. Jeba Sheela Easwari Engineering College
M. Krishnamurthy KCG College of Technology (India)

DOI:

https://doi.org/10.9781/ijimai.2025.09.003

Keywords:

Adaptive Scavenger-Based Dingo Optimization Algorithm, Diabetic Retinopathy Severity Classification, High-Ranking-Based Deep Ensemble Learning, Inception, Resnet, U-net, Architecture, VGG16

Abstract

Background problem: Diabetic Retinopathy (DR) is characterized by high glucose levels in the blood, which can lead to permanent vision loss and microvascular complications. Various deep learning techniques for DR analysis tend to be more complex and may experience delays in delivering accurate results, thereby limiting their application in clinical settings. Implementing real-time predictionand severity analysisof DR can address this problem by providing real-time diagnostic insights based on DR severity levels.
Aim: So, this paper is intended to offer a new DR detection and severity classification model with the highranking-based ensemble learning approach.
Methodology: The preprocessed and segmented images are utilized in the feature extraction processusing ensemble architecture which incorporated VGG16, Resnet, and Inception to get three sets of features. The optimal features are selected using an Adaptive Scavenger-Based Dingo Optimization Algorithm (AS-DOX) to achieve the efficient classification of DR severity. The optimization constraint stake place in the HighRanking-Based Deep Ensemble Learning (HR-DEL) model helps to enhance the efficacy of classification for the offered approach. The simulation analysis provides enhanced performance with the accurate classification of the designed DR severity classification approach by comparing it with other baseline methods.
Result: From the result analysis, the offered method achieves 96.6 % accuracy and sensitivity rate. Moreover, it achieves a 90.52% precision rate.
Conclusion: Thus, the designed DR severity classification model attains better performance, and also it is utilized for early detection of DR severity.

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Author Biographies

A. Jeba Sheela, Easwari Engineering College

Mrs. A. Jeba Sheela is working as a Assistant professor and Part time Research Scholar, Department of Computer Science and Engineering, Easwari Engineering College, Chennai. She received her M.E. degree in Computer Science and Engineering from Madha Engineering College, Anna University, Chennai, in 2010. She received the B.Tech degree from St.Xaviers’s catholic college of Engineering, Nagercoil, in 2007. Her current research interests in the areas of Artificial Intelligence, Deep Learning, Machine Learning and Digital Image Processing.

M. Krishnamurthy, KCG College of Technology (India)

Dr. M. Krishnamurthy is currently working as a Professor and Head in the Department of Artificial Intelligence and Data Science, KCG College of Technology, Chennai, India. He is having immense teaching and research experience of about 31 years. He is graduated with a master’s degree in Computer Science (1989) from AVC College, Mayiladurai, M.E degree in Computer Science and Engineering (2004) from Sri Venkateswara College of Engineering, Sriperumbudur and a Ph. D degree (2012) in the Faculty of Information and Communication Engineering at College of Engineering, Guindy. He leads a research group of Computer Science and Engineering faculty in KCG. To his credit, he has 3 patents granted, 22 books written for TNOU, 54+ SCI, Scopus, and other Indexed Journal publications and 14 publications in conference proceedings. His citation is 100, h-index 5, i10-index 4. His primary work and research interests include large database mining techniques, incremental data mining techniques, and social network, big data analysis, machine learning and deep learning. He is a recognized supervisor in Anna University and at present guiding candidates for research.

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