A Lightweight Neural Network Model for Disease Risk Prediction in Edge Intelligent Computing Architecture

Future Internet Pub Date : 2024-02-26 DOI:10.3390/fi16030075
Feng Zhou, Shijing Hu, Xin Du, Xiaoli Wan, Jie Wu
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引用次数: 1

Abstract

In the current field of disease risk prediction research, there are many methods of using servers for centralized computing to train and infer prediction models. However, this centralized computing method increases storage space, the load on network bandwidth, and the computing pressure on the central server. In this article, we design an image preprocessing method and propose a lightweight neural network model called Linge (Lightweight Neural Network Models for the Edge). We propose a distributed intelligent edge computing technology based on the federated learning algorithm for disease risk prediction. The intelligent edge computing method we proposed for disease risk prediction directly performs prediction model training and inference at the edge without increasing storage space. It also reduces the load on network bandwidth and reduces the computing pressure on the server. The lightweight neural network model we designed has only 7.63 MB of parameters and only takes up 155.28 MB of memory. In the experiment with the Linge model compared with the EfficientNetV2 model, the accuracy and precision increased by 2%, the recall rate increased by 1%, the specificity increased by 4%, the F1 score increased by 3%, and the AUC (Area Under the Curve) value increased by 2%.
边缘智能计算架构中用于疾病风险预测的轻量级神经网络模型
在当前的疾病风险预测研究领域,有许多方法是利用服务器进行集中计算,以训练和推断预测模型。然而,这种集中计算方法会增加存储空间、网络带宽负荷和中央服务器的计算压力。在本文中,我们设计了一种图像预处理方法,并提出了一种名为 Linge(边缘轻量级神经网络模型)的轻量级神经网络模型。我们提出了一种基于联合学习算法的分布式智能边缘计算技术,用于疾病风险预测。我们提出的疾病风险预测智能边缘计算方法可直接在边缘执行预测模型的训练和推理,而无需增加存储空间。同时,它还能减少对网络带宽的负载,减轻服务器的计算压力。我们设计的轻量级神经网络模型只有 7.63 MB 的参数,仅占用 155.28 MB 的内存。在使用 Linge 模型的实验中,与 EfficientNetV2 模型相比,准确度和精确度提高了 2%,召回率提高了 1%,特异性提高了 4%,F1 分数提高了 3%,AUC(曲线下面积)值提高了 2%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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