Open Access

ARTICLE

Stage-Wise Categorization and Prediction of Diabetic Retinopathy Using Ensemble Learning and 2D-CNN

N. M. Balamurugan^1,*, K. Maithili², T. K. S. Rathish Babu³, M. Adimoolam⁴

1 Department of Computer Science and Engineering, Sri Venkateswara College of Engineering, Sriperumbudur, Chennai, India
2 Department of Computer Science and Engineering, KG Reddy College of Engineering and Technology (Autonomous), Hyderabad, Telangana, India
3 Department of Computer Science and Engineering, Sridevi Women’s Engineering College, Hyderabad, Telangana, India
4 Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Thandalam, Chennai, India

* Corresponding Author: N. M. Balamurugan. Email:

Intelligent Automation & Soft Computing 2023, 36(1), 499-514. https://doi.org/10.32604/iasc.2023.031661

Received 23 April 2022; Accepted 22 June 2022; Issue published 29 September 2022

Abstract

Diabetic Eye Disease (DED) is a fundamental cause of blindness in human beings in the medical world. Different techniques are proposed to forecast and examine the stages in Prognostication of Diabetic Retinopathy (DR). The Machine Learning (ML) and the Deep Learning (DL) algorithms are the predominant techniques to project and explore the images of DR. Even though some solutions were adapted to challenge the cause of DR disease, still there should be an efficient and accurate DR prediction to be adapted to refine its performance. In this work, a hybrid technique was proposed for classification and prediction of DR. The proposed hybrid technique consists of Ensemble Learning (EL), 2 Dimensional-Conventional Neural Network (2D-CNN), Transfer Learning (TL) and Correlation method. Initially, the Stochastic Gradient Boosting (SGB) EL method was used to predict the DR. Secondly, the boosting based EL method was used to predict the DR of images. Thirdly 2D-CNN was applied to categorize the various stages of DR images. Finally, the TL was adopted to transfer the classification prediction to training datasets. When this TL was applied, a new prediction feature was increased. From the experiment, the proposed technique has achieved 97.8% of accuracy in prophecies of DR images and 98% accuracy in grading of images. The experiment was also extended to measure the sensitivity (99.6%) and specificity (97.3%) metrics. The predicted accuracy rate was compared with existing methods.

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Keywords

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