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Game Theory-Based Dynamic Weighted Ensemble for Retinal Disease Classification

Kanupriya Mittal*, V. Mary Anita Rajam

Department of CSE, CEG, Anna University, Chennai, 600025, India

* Corresponding Author: Kanupriya Mittal. Email: email

Intelligent Automation & Soft Computing 2023, 35(2), 1907-1921. https://doi.org/10.32604/iasc.2023.029037

Abstract

An automated retinal disease detection system has long been in existence and it provides a safe, no-contact and cost-effective solution for detecting this disease. This paper presents a game theory-based dynamic weighted ensemble of a feature extraction-based machine learning model and a deep transfer learning model for automatic retinal disease detection. The feature extraction-based machine learning model uses Gaussian kernel-based fuzzy rough sets for reduction of features, and XGBoost classifier for the classification. The transfer learning model uses VGG16 or ResNet50 or Inception-ResNet-v2. A novel ensemble classifier based on the game theory approach is proposed for the fusion of the outputs of the transfer learning model and the XGBoost classifier model. The ensemble approach significantly improves the accuracy of retinal disease prediction and results in an excellent performance when compared to the individual deep learning and feature-based models.

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Cite This Article

K. Mittal and V. Mary Anita Rajam, "Game theory-based dynamic weighted ensemble for retinal disease classification," Intelligent Automation & Soft Computing, vol. 35, no.2, pp. 1907–1921, 2023.



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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