Diabetic retinopathy detection and categorizing using a lightweight deep learning approach

D. Praneeth; N. Satheesh Kumar

doi:10.1016/j.compbiomed.2021.10

Author(s)

D. Praneeth Computer Science and Engineering, Chaitanya (Deemed to be University),Telangana,India

N. Satheesh Kumar Computer Science and Engineering, Chaitanya (Deemed to be University),Telangana,India

Abstract

Diabetic retinopathy is an ocular disorder that has the potential to result in visual impairment and complete loss of vision in those diagnosed with diabetes. This illness affects the retinal blood vessels inside the light-sensitive tissue layer at the posterior of the eye, known as the retina. This paper presents a complete approach to diagnosing and categorizing diabetic retinopathy using deep learning models. A lightweight Convolutional Neural Network (CNN) is used to detect diabetic retinopathy in fundus images. This CNN has been developed to have fewer parameters and calculations, making it suited for resource-constrained environments while retaining decent performance. The categorization of diabetic retinopathy is carried out with the help of EfficientNet. This model uses an innovative compound scaling approach to strike a balance between the model's depth, width, and resolution. As a result, it maximizes computing efficiency while preserving high accuracy. The proposed detection model obtained an accuracy of 95%, and the classification model produced an accuracy of 84%.

Publication Details

Page(s) 854-863

DOI 10.1016/j.compbiomed.2021.10

Published Journal: ARPN Journal of Engineering and Applied Sciences, Volume: 19, Issue: 13, Year: 2024

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Keywords

Diabetic Retinopathy, Classification, detection, Convolutional Neural Network, EfficientNet

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