MedCheX:一种高效的COVID-19临床检测模型

IF 0.5 4区计算机科学 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS

Journal of Information Science and Engineering Pub Date : 2022-01-01 DOI:10.6688/jise.202207_38

Chi-Shiang Wang, Fang-Yi Su, J. Chiang

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引用次数: 1

摘要

由于新冠肺炎传染性强、潜伏期长，疫情爆发以来，高效准确地检测新冠肺炎至关重要。我们提出了一种新的基于unet++的检测模型，采用密集块作为编码器。该模型不仅可以对COVID-19进行检测和分类，而且可以精确地分割病变区域。我们还设计了两阶段训练策略以及自定义组，特别是心后病变，以使模型具有鲁棒性。我们在COVID-19开放数据集上获得了0.868的精度，0.920的召回率和0.893的f1得分。为了对这场大流行做出贡献，我们用我们的模型建立了一个网站(https://medchex.tech/)。

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MedCheX: An Efficient COVID-19 Detection Model for Clinical Usage

Due to the highly infectious and long incubation period of COVID-19, detecting COVID-19 efficiently and accurately is crucial since the epidemic outbreak. We proposed a new detection model based on U-Net++ and adopted dense blocks as the encoder. The model not only detects and classifies COVID-19 but also segment the lesion area precisely. We also designed a two-phase training strategy along with self-defined groups, especially the retrocardiac lesion to make model robust. We achieved 0.868 precision, 0.920 recall, and 0.893 F1-score on the COVID-19 open dataset. To contribute to this pandemic, we have set up a website with our model (https://medchex.tech/).

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来源期刊

Journal of Information Science and Engineering 工程技术-计算机：信息系统

CiteScore

2.00

自引率

0.00%

发文量

审稿时长

8 months

期刊介绍： The Journal of Information Science and Engineering is dedicated to the dissemination of information on computer science, computer engineering, and computer systems. This journal encourages articles on original research in the areas of computer hardware, software, man-machine interface, theory and applications. tutorial papers in the above-mentioned areas, and state-of-the-art papers on various aspects of computer systems and applications.