A Hybrid Ensemble Feature Selection-based Segmentation and Deep Majority Voting Framework on Large Multi-class Diabetes Retinopathy Databases

Abstract

Diabetic retinopathy is a micro vascular disease that induces a number of changes in the retina. Micro aneurysms, haemorrhage exudates, and the development of new blood vessels all alter the diameter of the blood vessel. Most of the conventional multi-class diabetes retinopathy has different issues such as problem of over-segmentation, classification precision, recall and error rate on high dimensional features space.  Ensemble feature selection measures are used to filter the essential features in the large feature space. In this work, a hybrid ensemble feature selection based multiple classification models are used to improve the classification accuracy on multi-class diabetes retinopathy databases. In this work, a novel image segmentation, ensemble feature extraction measures, and multiple classification approaches are used to find the majority voting in the classification problem. Experimental results show that the proposed ensemble feature extraction-based voting classification model has better efficiency compared to the state of art of conventional approaches.