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. 2021;51(2):854-864.
doi: 10.1007/s10489-020-01829-7. Epub 2020 Sep 5.

V体育官网入口 - Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network

Asmaa Abbas  1 , Mohammed M Abdelsamea  1   2 , Mohamed Medhat Gaber  2
Affiliations

"V体育官网入口" Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network

Asmaa Abbas et al. Appl Intell (Dordr). 2021.

Abstract

Chest X-ray is the first imaging technique that plays an important role in the diagnosis of COVID-19 disease. Due to the high availability of large-scale annotated image datasets, great success has been achieved using convolutional neural networks (CNN s) for image recognition and classification. However, due to the limited availability of annotated medical images, the classification of medical images remains the biggest challenge in medical diagnosis VSports手机版. Thanks to transfer learning, an effective mechanism that can provide a promising solution by transferring knowledge from generic object recognition tasks to domain-specific tasks. In this paper, we validate and a deep CNN, called Decompose, Transfer, and Compose (DeTraC), for the classification of COVID-19 chest X-ray images. DeTraC can deal with any irregularities in the image dataset by investigating its class boundaries using a class decomposition mechanism. The experimental results showed the capability of DeTraC in the detection of COVID-19 cases from a comprehensive image dataset collected from several hospitals around the world. High accuracy of 93. 1% (with a sensitivity of 100%) was achieved by DeTraC in the detection of COVID-19 X-ray images from normal, and severe acute respiratory syndrome cases. .

Keywords: COVID-19 detection; Chest X-ray images; Covolutional neural networks; Data irregularities; DeTraC V体育安卓版. .

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Figures

Fig. 1
Fig. 1
Examples of a) normal, b) COVID-19, and c) SARS chest X-ray images
Fig. 2
Fig. 2
De compose, Tra nsfer, and C ompose (DeTraC) model for the detection of COVID-19 from CXR images
Fig. 3
Fig. 3
The learning curve accuracy (a) and error (b) obtained by DeTraC model when VGG19 is used as a backbone pre-trained model
Fig. 4
Fig. 4
The ROC analysis curve by training DeTraC model based on VGG19 pre-trained model
Fig. 5
Fig. 5
The accuracy (a) and sensitivity (b), on both original and augmented test cases, obtained by DeTraC model when compared to different pre-trained models, in shallow- and deep-tuning modes
Fig. 6
Fig. 6
The accuracy (a) and sensitivity (b), on the original test cases only, obtained by DeTraC model when compared to different pre-trained models, in shallow- and deep-tuning modes
See this image and copyright information in PMC

References

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