Segmentation and Classification of Diabetic Retinopathy using Ensemble Deep Neural Network

Abstract

Diabetic Retinopathy (DR) is an eye ailment in diabetics, often causing vision loss. Timely retinal screenings can help prevent blindness. While DR cannot be cured, early detection can curb vision decline. Ophthalmologists diagnose DR using Optical Coherence Tomography (OCT) or Fundus Images. Evaluating via fundus images can be complex and potentially imprecise. The study offers a computer-supported diagnosis to aid ophthalmologists. It uses Multi-level Otsu thresholding to segment Fundus Images, with segmented outputs processed further. Advanced feature extraction techniques like the Gray-Level Co-occurrence Matrix (GLCM) and dynamic Flemingo optimization enhance DR feature identification. Additionally, a novel cascaded voting ensemble deep neural network model is introduced, merging multiple algorithmic insights to improve classification. The paper concludes by aligning classifications with a standard grading system, giving clinicians a clear DR severity gauge for better treatment planning.