Using X-ray images and deep learning for automated detection of coronavirus disease
Using X-ray images and deep learning for automated detection of coronavirus disease
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DOI：
10.1080/07391102.2020.1767212
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发表时间：
2020-05-21
影响因子：
4.4
通讯作者：
Chawki, Youness
中科院分区：
文献类型：
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作者：
El Asnaoui, Khalid;Chawki, Youness
冠状病毒仍然是全球死亡的主要原因。由于病例每天都在增加，急诊诊所提供了一定数量的COVID-19检测单位。因此，为了防止新冠病毒在个人之间传播，有必要实施自动检测和分类系统，作为一种快速的选择性发现选择。医学图像分析是最有前途的研究领域之一，它为冠状病毒等多种疾病的诊断和决策提供了便利。本文对近年来深度学习模型（VGG16、VGG19、DenseNet201、Inception_ResNet_V2、Inception_V3、Resnet50和MobileNet_V2）在冠状病毒肺炎检测与分类中的应用进行了对比研究。实验使用6087张胸片和CT数据集（细菌性肺炎2780张，冠状病毒1493张，新冠病毒231张，正常人1583张），使用混淆矩阵对模型性能进行评价。结果发现，使用inception - resnet_v2和Densnet201的结果比使用其他模型更好（Inception-ResNetV2的准确率为92.18%，Densnet201的准确率为88.09%）。由Ramaswamy H. Sarma传达
Coronavirus is still the leading cause of death worldwide. There are a set number of COVID-19 test units accessible in emergency clinics because of the expanding cases daily. Therefore, it is important to implement an automatic detection and classification system as a speedy elective finding choice to forestall COVID-19 spreading among individuals. Medical images analysis is one of the most promising research areas, it provides facilities for diagnosis and making decisions of a number of diseases such as Coronavirus. This paper conducts a comparative study of the use of the recent deep learning models (VGG16, VGG19, DenseNet201, Inception_ResNet_V2, Inception_V3, Resnet50, and MobileNet_V2) to deal with detection and classification of coronavirus pneumonia. The experiments were conducted using chest X-ray & CT dataset of 6087 images (2780 images of bacterial pneumonia, 1493 of coronavirus, 231 of Covid19, and 1583 normal) and confusion matrices are used to evaluate model performances. Results found out that the use of inception_Resnet_V2 and Densnet201 provide better results compared to other models used in this work (92.18% accuracy for Inception-ResNetV2 and 88.09% accuracy for Densnet201). Communicated by Ramaswamy H. Sarma