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Detection of five severity levels of diabetic retinopathy using ensemble deep learning model

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Abstract

People who have diabetes can develop diabetic retinopathy, a condition that adversely affects the eyes. Detecting diabetic retinopathy early on allows appropriate treatment to be administered based on the severity of the condition. Our main objective is to determine the severity of the disease using fundus photographs taken under a variety of imaging conditions. This is achieved using an ensemble of convolutional models. Initially, convolutional neural networks are trained on the Diabetic Retinopathy dataset, and then they are stacked to form an ensemble, and again trained against the dataset and five labels. The validation accuracy of the ensemble model is 87.31%. The model outputs the corresponding labels (No-DR, Mild, Moderate, Severe, Proliferate-DR) indicating the degree of severity of the disease.

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Data Availability

The Rokach, L [30] and Aptos [1] datasets.

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  1. Indian Institute of Information Technology, Pune, India

    Yatharth Kale & Sanjeev Sharma

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  1. Yatharth Kale
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Kale, Y., Sharma, S. Detection of five severity levels of diabetic retinopathy using ensemble deep learning model. Multimed Tools Appl 82, 19005–19020 (2023). https://doi.org/10.1007/s11042-022-14277-x

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