Game Theory-Based Dynamic Weighted Ensemble for Retinal Disease Classification

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 extractionbased 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.

DeepRetNet: Retinal Disease Classification using Attention UNet++ based Segmentation and Optimized Deep Learning Technique

“…) ( , (7) where, the lower and upper bounded limits are defined as s D and s C and the random value is notated as Rand . The fitness of the solution is defined as elite and is measured based on the mean square error.…”

“…However, the diagnosis of retinal illnesses was often performed by analysing and interpreting the collected retina images; as a result, the process might take a while, even for skilled ophthalmologists [5,6]. The global increase of people with eye conditions, however, may make the issue of delayed illness identification worse [7].…”