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一种基于迁移学习的深度卷积神经网络方法用于新冠肺炎和肺炎感染的胸部X光图像分类。

A Transfer Learning-Based Approach with Deep CNN for COVID-19- and Pneumonia-Affected Chest X-ray Image Classification.

作者信息

Chakraborty Soarov, Paul Shourav, Hasan K M Azharul

机构信息

Department of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna, 9203 Bangladesh.

出版信息

SN Comput Sci. 2022;3(1):17. doi: 10.1007/s42979-021-00881-5. Epub 2021 Oct 26.

DOI:10.1007/s42979-021-00881-5
PMID:34723208
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8547126/
Abstract

The COVID-19 pandemic creates a significant impact on everyone's life. One of the fundamental movements to cope with this challenge is identifying the COVID-19-affected patients as early as possible. In this paper, we classified COVID-19, Pneumonia, and Healthy cases from the chest X-ray images by applying the transfer learning approach on the pre-trained VGG-19 architecture. We use MongoDB as a database to store the original image and corresponding category. The analysis is performed on a public dataset of 3797 X-ray images, among them COVID-19 affected (1184 images), Pneumonia affected (1294 images), and Healthy (1319 images) (https://www.kaggle.com/tawsifurrahman/covid19-radiography-database/version/3). This research gained an accuracy of 97.11%, average precision of 97%, and average Recall of 97% on the test dataset.

摘要

新冠疫情对每个人的生活都产生了重大影响。应对这一挑战的一项基本举措是尽早识别出感染新冠病毒的患者。在本文中，我们通过在预训练的VGG - 19架构上应用迁移学习方法，从胸部X光图像中对新冠肺炎、肺炎和健康病例进行分类。我们使用MongoDB作为数据库来存储原始图像及相应类别。分析是在一个包含3797张X光图像的公共数据集上进行的，其中感染新冠病毒的有1184张图像，感染肺炎的有1294张图像，健康的有1319张图像（https://www.kaggle.com/tawsifurrahman/covid19 - radiography - database/version/3）。这项研究在测试数据集上获得了97.11%的准确率、97%的平均精确率和97%的平均召回率。

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