AN EFFICIENT FEATURE EXTRACTION AND CLASSIFICATION BASED MULTI-LEVEL. DEEP LEARNING FRAMEWORK FOR DIABETIC RETINOPATHY DETECTION

      Аннотация: This book provides An Efficient Feature Extraction and Classification based Multi –level Deep Learning Frame work for Diabetic Retinopathy Detection model has better efficiency compared to the state of art of conventional approaches. 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.

Ключевые слова: Diabetic retinopathy, Microaneurysms, Convolution Neural Networks, hybrid SVM, Ensemble feature selection, Image segmentation