Deploying and Evaluating Multiple Deep Learning Models on Edge Devices for Diabetic Retinopathy Detection

• URL: http://arxiv.org/abs/2506.14834v1
• Date: Sat, 14 Jun 2025 13:53:45 GMT
• Title: Deploying and Evaluating Multiple Deep Learning Models on Edge Devices for Diabetic Retinopathy Detection
• Authors: Akwasi Asare, Dennis Agyemanh Nana Gookyi, Derrick Boateng, Fortunatus Aabangbio Wulnye,
• Abstract summary: Diabetic Retinopathy (DR) affects approximately 34.6% of diabetes patients globally, with the number of cases projected to reach 242 million by 2045.<n>Traditional DR diagnosis relies on the manual examination of retinal fundus images, which is both time-consuming and resource intensive.<n>This study presents a novel solution using Edge Impulse to deploy multiple deep learning models for real-time DR on edge devices.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Diabetic Retinopathy (DR), a leading cause of vision impairment in individuals with diabetes, affects approximately 34.6% of diabetes patients globally, with the number of cases projected to reach 242 million by 2045. Traditional DR diagnosis relies on the manual examination of retinal fundus images, which is both time-consuming and resource intensive. This study presents a novel solution using Edge Impulse to deploy multiple deep learning models for real-time DR detection on edge devices. A robust dataset of over 3,662 retinal fundus images, sourced from the Kaggle EyePACS dataset, was curated, and enhanced through preprocessing techniques, including augmentation and normalization. Using TensorFlow, various Convolutional Neural Networks (CNNs), such as MobileNet, ShuffleNet, SqueezeNet, and a custom Deep Neural Network (DNN), were designed, trained, and optimized for edge deployment. The models were converted to TensorFlowLite and quantized to 8-bit integers to reduce their size and enhance inference speed, with minimal trade-offs in accuracy. Performance evaluations across different edge hardware platforms, including smartphones and microcontrollers, highlighted key metrics such as inference speed, accuracy, precision, and resource utilization. MobileNet achieved an accuracy of 96.45%, while SqueezeNet demonstrated strong real-time performance with a small model size of 176 KB and latency of just 17 ms on GPU. ShuffleNet and the custom DNN achieved moderate accuracy but excelled in resource efficiency, making them suitable for lower-end devices. This integration of edge AI technology into healthcare presents a scalable, cost-effective solution for early DR detection, providing timely and accurate diagnosis, especially in resource-constrained and remote healthcare settings.

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