标题： 在边缘设备上部署和评估用于糖尿病视网膜病变检测的多种深度学习模型 显示英文标题

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

摘要： 糖尿病视网膜病变（DR）是糖尿病患者视力损伤的主要原因，全球约有34.6%的糖尿病患者受到影响，预计到2045年病例数将达到2.42亿。 传统的DR诊断依赖于视网膜眼底图像的手动检查，这种方法既耗时又资源密集。 本研究提出了一种使用Edge Impulse在边缘设备上部署多个深度学习模型以实现实时DR检测的新解决方案。 一个包含超过3,662张视网膜眼底图像的强大数据集，来源于Kaggle EyePACS数据集，并通过包括增强和归一化在内的预处理技术进行了整理和优化。 利用TensorFlow，设计、训练并优化了多种卷积神经网络（CNNs），如MobileNet、ShuffleNet、SqueezeNet以及一个自定义的深度神经网络（DNN），以适应边缘部署。 这些模型被转换为TensorFlowLite并量化为8位整数，以减小模型大小并提高推理速度，同时在准确率上的折衷最小。 在不同边缘硬件平台（包括智能手机和微控制器）上的性能评估突出了推断速度、准确率、精度和资源利用率等关键指标。 MobileNet实现了96.45%的准确率，而SqueezeNet凭借仅为176 KB的小模型尺寸和GPU上仅17毫秒的延迟，展示了强大的实时性能。 ShuffleNet和自定义DNN取得了适中的准确率，但在资源效率方面表现出色，使其适用于低端设备。 这种将边缘AI技术整合到医疗保健中的方式为早期DR检测提供了一个可扩展且具有成本效益的解决方案，特别是在资源匮乏和偏远的医疗环境中，能够提供及时且准确的诊断。 显示英文摘要

摘要： 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.