SCovNet:基于跳跃连接的特征联合深度学习技术与统计方法分析,用于COVID-19检测

IF 5.3 2区 医学 Q1 ENGINEERING, BIOMEDICAL
Kiran Kumar Patro , Allam Jaya Prakash , Mohamed Hammad , Ryszard Tadeusiewicz , Paweł Pławiak
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引用次数: 11

摘要

背景与目的新冠肺炎疫情对全球人口造成严重影响。感染在世界各地迅速传播,新的高峰(德尔塔、德尔塔+和奥密克戎)仍在出现。实时逆转录聚合酶链式反应(RT-PCR)是最常用于在鼻咽拭子中发现病毒RNA的方法。然而,这些诊断方法需要人类参与,并且每次预测需要花费更多的时间。此外,现有的常规检测主要存在假阴性,因此病毒有可能迅速传播。因此,需要对新冠肺炎患者进行快速、早期诊断,以克服这些问题。方法现有的基于深度学习的新冠病毒检测方法存在数据集不平衡、性能差和梯度消失问题。为了克服上述一些问题,本文开发了一种具有特征并集方法的基于跳过连接的定制网络。从胸部X射线(CXR)图像到后续层的梯度信息通过跳过连接绕过。在脚本的标题中,“SCovNet”指的是一个基于skip-connection的功能联合网络,用于检测新冠肺炎。使用两个公开可用的CXR图像数据库测试了所提出模型的性能,包括平衡和不平衡数据集。结果针对一个小的不平衡数据集(Kaggle),提出了一种改进的基于跳跃连接的CNN模型,并取得了显著的性能。此外,所提出的模型还用大型GitHub CXR图像数据库进行了测试,总体最佳准确率为98.67%,假阴性率为0.0074。作为一个额外的兴趣点,我们必须提到为这项工作提供的创新分层分类策略,该策略考虑了平衡和不平衡的数据集,以获得最佳的新冠肺炎识别率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

SCovNet: A skip connection-based feature union deep learning technique with statistical approach analysis for the detection of COVID-19

SCovNet: A skip connection-based feature union deep learning technique with statistical approach analysis for the detection of COVID-19

SCovNet: A skip connection-based feature union deep learning technique with statistical approach analysis for the detection of COVID-19

SCovNet: A skip connection-based feature union deep learning technique with statistical approach analysis for the detection of COVID-19

Background and Objective

The global population has been heavily impacted by the COVID-19 pandemic of coronavirus. Infections are spreading quickly around the world, and new spikes (Delta, Delta Plus, and Omicron) are still being made. The real-time reverse transcription-polymerase chain reaction (RT-PCR) is the method most often used to find viral RNA in a nasopharyngeal swab. However, these diagnostic approaches require human involvement and consume more time per prediction. Moreover, the existing conventional test mainly suffers from false negatives, so there is a chance for the virus to spread quickly. Therefore, a rapid and early diagnosis of COVID-19 patients is needed to overcome these problems.

Methods

Existing approaches based on deep learning for COVID detection are suffering from unbalanced datasets, poor performance, and gradient vanishing problems. A customized skip connection-based network with a feature union approach has been developed in this work to overcome some of the issues mentioned above. Gradient information from chest X-ray (CXR) images to subsequent layers is bypassed through skip connections. In the script’s title, “SCovNet” refers to a skip-connection-based feature union network for detecting COVID-19 in a short notation. The performance of the proposed model was tested with two publicly available CXR image databases, including balanced and unbalanced datasets.

Results

A modified skip connection-based CNN model was suggested for a small unbalanced dataset (Kaggle) and achieved remarkable performance. In addition, the proposed model was also tested with a large GitHub database of CXR images and obtained an overall best accuracy of 98.67% with an impressive low false-negative rate of 0.0074.

Conclusions

The results of the experiments show that the proposed method works better than current methods at finding early signs of COVID-19. As an additional point of interest, we must mention the innovative hierarchical classification strategy provided for this work, which considered both balanced and unbalanced datasets to get the best COVID-19 identification rate.

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来源期刊
CiteScore
16.50
自引率
6.20%
发文量
77
审稿时长
38 days
期刊介绍: Biocybernetics and Biomedical Engineering is a quarterly journal, founded in 1981, devoted to publishing the results of original, innovative and creative research investigations in the field of Biocybernetics and biomedical engineering, which bridges mathematical, physical, chemical and engineering methods and technology to analyse physiological processes in living organisms as well as to develop methods, devices and systems used in biology and medicine, mainly in medical diagnosis, monitoring systems and therapy. The Journal''s mission is to advance scientific discovery into new or improved standards of care, and promotion a wide-ranging exchange between science and its application to humans.
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