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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, ensemblefeature extraction measures, and multiple classification approaches are used to find the majority voting in the classification problem.