基于VGG19的标量Winograd电路的COVID-19肺部CT图像识别

IF 1.3 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS IET Networks Pub Date : 2022-10-14 DOI:10.1109/IET-ICETA56553.2022.9971486

Shao-Huai Wang, Pin-Chieh Hsieh, Tzu-Yao Su, Jhih-Yuan Gao, Min-Hua Lu, Yunqi Fan

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引用次数: 0

摘要

本文提出了基于VGG19的标量winograd电路的COVID-19肺部CT (computer tomography)图像识别方法。我们采用VGG-19机器学习架构对肺部CT图像进行识别，并通过超标量Winograd电路加快神经网络运算速度。经过一系列的实验，我们提出的方法具有较高的肺炎识别率和计算效率。

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COVID-19 Lung CT Images Recognition with Superscalar Winograd Circuit Based on VGG19

In this paper, we proposed COVID-19 lung CT (computed tomography) images recognition with superscalar winograd circuit based on VGG19. We adopt the VGG-19 machine learning architecture to recognize lung CT images and speed up neural network operations through Superscalar Winograd Circuit. After a series of experiments, our proposed method has a high pneumonia recognition rate and high computational efficiency.

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来源期刊

IET Networks COMPUTER SCIENCE, INFORMATION SYSTEMS-

CiteScore

5.00

自引率

0.00%

发文量

审稿时长

33 weeks

期刊介绍： IET Networks covers the fundamental developments and advancing methodologies to achieve higher performance, optimized and dependable future networks. IET Networks is particularly interested in new ideas and superior solutions to the known and arising technological development bottlenecks at all levels of networking such as topologies, protocols, routing, relaying and resource-allocation for more efficient and more reliable provision of network services. Topics include, but are not limited to: Network Architecture, Design and Planning, Network Protocol, Software, Analysis, Simulation and Experiment, Network Technologies, Applications and Services, Network Security, Operation and Management.