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2022 | OriginalPaper | Chapter

Automatic Detection of Diabetic Retinopathy on the Edge

Authors : Zahid Maqsood, Manoj Kumar Gupta

Published in: Cyber Security, Privacy and Networking

Publisher: Springer Nature Singapore

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Abstract

The uncontrolled blood sugar levels in diabetes patients lead to an eye disease called diabetic retinopathy. The high sugar levels in the blood vessels of the retina cause blockage of some blood vessels due to which fluids like plasma leak easily into the eye causing the lesions which appear in the eye and may cause severe vision problems. A severe vision problem can be prevented   by detecting and treating it at an early stage. In India alone, about 80 million people suffer from diabetes, and there is one ophthalmologist for every 100,000 population. Due to this serious shortage of well-trained ophthalmologists, it becomes difficult to diagnose the severity of diabetic retinopathy in some rural areas of India. Since most of the AI solutions for detecting diabetic retinopathy are cloud-based, therefore, it becomes difficult to deploy these frameworks in rural areas where there is no connectivity and no proper Internet connection. This paper focuses on energy-efficient and real-time detection of the severity of diabetic retinopathy on the low-powered edge device without any proper connectivity. In this paper, various deep transfer learning methods were investigated for DR detection, and these include ResNet50, Inceptionv3, EfficientNet-B5, EfficientNet-B6, and VGG19. These CNN models were trained on preprocessed APTOS dataset. To increase training data and to overcome overfitting, various data augmentation techniques were used. The highest accuracy of 86.03% was achieved by EfficientNet-B6.

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Metadata
Title
Automatic Detection of Diabetic Retinopathy on the Edge
Authors
Zahid Maqsood
Manoj Kumar Gupta
Copyright Year
2022
Publisher
Springer Nature Singapore
Book
Cyber Security, Privacy and Networking

Print ISBN: 978-981-16-8663-4

Electronic ISBN: 978-981-16-8664-1

Copyright Year: 2022

DOI
https://doi.org/10.1007/978-981-16-8664-1_12