[1]朱万鹏,雷秀娟.使用胸片检测新型冠状肺炎的深度集成网络[J].计算机技术与发展,2023,33(02):153-160.[doi:10. 3969 / j. issn. 1673-629X. 2023. 02. 023]
　ZHU Wan-peng,LEI Xiu-juan.Deep Integrated Network for Detection of COVID-19 Using Chest Radiographs[J].,2023,33(02):153-160.[doi:10. 3969 / j. issn. 1673-629X. 2023. 02. 023]
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使用胸片检测新型冠状肺炎的深度集成网络()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
33
期数:
2023年02期
页码:
153-160
栏目:
人工智能
出版日期:
2023-02-10

文章信息/Info

Title:
Deep Integrated Network for Detection of COVID-19 Using Chest Radiographs
文章编号:
1673-629X(2023)02-0153-08
作者:
朱万鹏; 雷秀娟
陕西师范大学 计算机科学学院,陕西 西安 710119
Author(s):
ZHU Wan-peng; LEI Xiu-juan
School of Computer Science,Shaanxi Normal University,Xi’an 710119,China
关键词:
深度学习; 集成网络; 新型冠状肺炎; 深度可分离卷积; 人工智能
Keywords:
deep learning; integrated network; COVID-19; depth separable convolution; artificial intelligence
分类号:
TP751
DOI:
10. 3969 / j. issn. 1673-629X. 2023. 02. 023
摘要:
随着新型冠状病毒的爆发,该病毒引起的疫情已成为全球医疗体系最大的威胁之一。 由于目前新型冠状肺炎的强传染性,导致感染人群较多,同时肺炎也成为了影响新型冠状病毒检测的主要因素之一,因而快速诊断检测已经成为主要的挑战。 胸部 X 光检测是一种安全、灵活、速度快和有利的检测方式。 该文提出了一种融合深度可分离网络、卷积自编码器和 VGG16 的深度集成网络,构建了一个强有力的分类模型,用于对新型冠状肺炎的检测与分类。 使用 Kaggle 存储库的 COVID-19 放射学标准数据集中的胸部 X 光图像进行验证。 实验结果表明,该模型对于 COVID-19、肺炎和正常的分类准确率为 96. 15% ,灵敏度为 98. 92% ,F1 评分为 94. 92% 。 最后,将该模型与现有的模型进行了实验性能对比,同时结合了基于梯度的鉴别定位来区分不同类型肺炎的 X 光图像的异常区域。 实验结果表明,提出的模型优于现有的模型,具有较好的鲁棒性,可以作为检测新型冠状肺炎的辅助工具。
Abstract:
With the outbreak of New Coronavirus,the epidemic caused by the virus has become one of the biggest threats to the globalmedical system. Due to the strong contagion of the new type of coronary pneumonia, a large number of people are infected, andpneumonia has become one of the main factors that affect the detection of New Coronavirus. Therefore,rapid diagnosis and detection hasbecome a major challenge. Chest X-ray detection is a safe,flexible,fast and favorable detection method. We propose a deep integrationnetwork integrating deep separable network,convolutional self-encoder and VGG16,and construct a powerful classification model for thedetection and classification of new coronary pneumonia. We use chest X-ray images from the COVID-19 radiology standard dataset of　the Kaggle repository for validation. The experiment shows that the classification accuracy of COVID - 19, pneumonia and normal is　96．15% ,the sensitivity is 　98. 92% ,and the F1 score is 94. 92% . Finally,the experimental performance of the model is compared withthe existing models,and the gradient based differential localization is combined to distinguish the abnormal areas of X - ray images ofdifferent types of pneumonia. The experimental results show that the proposed model is superior to the existing models with goodrobustness,which can be used as an auxiliary tool for the detection of new coronary pneumonia.

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更新日期/Last Update: 2023-02-10