CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images
CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images
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DOI：
10.1016/j.cmpb.2020.105581
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发表时间：
2020-11-01
影响因子：
6.1
通讯作者：
Bhat, Mohammad Mudasir
中科院分区：
文献类型：
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作者：
Khan, Asif Iqbal;Shah, Junaid Latief;Bhat, Mohammad Mudasir
背景和目的：新型冠状病毒也称为 COVID-19，于 2019 年 12 月起源于中国武汉，现已在世界范围内传播。迄今为止，该病毒已感染约 180 万人，并夺去了约 114,698 人的生命。随着病例数量迅速增加，大多数国家都面临检测试剂盒和资源短缺的问题。有限的检测试剂盒数量和不断增加的每日病例数量促使我们提出一种深度学习模型，可以帮助放射科医生和临床医生使用胸部 X 光检查检测 COVID-19 病例。方法：在这项研究中，我们提出了 CoroNet，一种深度卷积神经网络模型，可以从胸部 X 光图像自动检测 COVID-19 感染。所提出的模型基于在 ImageNet 数据集上预训练的 Xception 架构，并在通过从两个不同的公开数据库收集 COVID-19 和其他胸部肺炎 X 射线图像而准备的数据集上进行端到端训练。结果：CoroNet 已经在准备好的数据集上进行了训练和测试，实验结果表明我们提出的模型实现了 89.6% 的总体准确率，更重要的是，对于 4 类，COVID-19 病例的精确率和召回率分别为 93% 和 98.2%病例（新冠肺炎、肺炎细菌、肺炎病毒、正常）。对于 3 类分类（新冠肺炎、肺炎、正常），所提出的模型产生了 95% 的分类准确率。这项研究的初步结果看起来很有希望，随着更多训练数据的出现，可以进一步改进。结论：CoroNet 在准备好的小型数据集上取得了有希望的结果，这表明给定更多数据，所提出的模型可以通过最少的数据预处理获得更好的结果。总体而言，所提出的模型极大地推进了当前基于放射学的方法，并且在 COVID-19 大流行期间，它可以成为临床医生和放射科医生非常有用的工具，帮助他们诊断、量化和跟踪 COVID-19 病例。 (C) 2020 Elsevier B.V. 保留所有权利。
Background and Objective: The novel Coronavirus also called COVID-19 originated in Wuhan, China in December 2019 and has now spread across the world. It has so far infected around 1.8 million people and claimed approximately 114,698 lives overall. As the number of cases are rapidly increasing, most of the countries are facing shortage of testing kits and resources. The limited quantity of testing kits and increasing number of daily cases encouraged us to come up with a Deep Learning model that can aid radiologists and clinicians in detecting COVID-19 cases using chest X-rays.Methods: In this study, we propose CoroNet, a Deep Convolutional Neural Network model to automatically detect COVID-19 infection from chest X-ray images. The proposed model is based on Xception architecture pre-trained on ImageNet dataset and trained end-to-end on a dataset prepared by collecting COVID-19 and other chest pneumonia X-ray images from two different publically available databases.Results: CoroNet has been trained and tested on the prepared dataset and the experimental results show that our proposed model achieved an overall accuracy of 89.6%, and more importantly the precision and recall rate for COVID-19 cases are 93% and 98.2% for 4-class cases (COVID vs Pneumonia bacterial vs pneumonia viral vs normal). For 3-class classification (COVID vs Pneumonia vs normal), the proposed model produced a classification accuracy of 95%. The preliminary results of this study look promising which can be further improved as more training data becomes available.Conclusion: CoroNet achieved promising results on a small prepared dataset which indicates that given more data, the proposed model can achieve better results with minimum pre-processing of data. Overall, the proposed model substantially advances the current radiology based methodology and during COVID19 pandemic, it can be very helpful tool for clinical practitioners and radiologists to aid them in diagnosis, quantification and follow-up of COVID-19 cases. (C) 2020 Elsevier B.V. All rights reserved.