Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network

By Asmaa Abbas, Mohammed M. Abdelsamea, Mohamed Medhat Gaber. Applied intelligence (Boston), 2020

917 citations recorded by the source corpus.

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. 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.

Subfields: Artificial Intelligence, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/a15b28c5b9e2d3842fdb9893be668bc51d63dfbe

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In Artificial Intelligence

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Mapping the Landscape of Artificial Intelligence Applications against COVID-19 (2020, methodology): “As shown in [71] [72] [73] † , there is potential in the use of Deep Learning approaches on X-Ray imagery, using architectures similar to the ones used for CT scans (e.g., ResNet [13] and Convolutional Neural Networks (CNNs) [28] ).”
A review on deep learning in medical image analysis (2021, methodology): “This could pave the way for personalized medicine (also known as precision medicine), in which each person’s genetic, environmental, and lifestyle factors are considered for disease prevention, treatment, and prognosis [17].”
Automated Deep Transfer Learning-Based Approach for Detection of COVID-19 Infection in Chest X-rays (2020, positive): “[18] presented a deep CNN named as Decompose, Transfer, and Compose (DeTraC) for distinguishing the symptoms of COVID-19 using chest X-rays.”
Can AI Help in Screening Viral and COVID-19 Pneumonia? (2020, methodology): “[44] have worked on a very small database of 105 COVID-19, 80 Normal and 11 SARS X-ray images to detect COVID-19 X-ray images using modified pre-trained CNN model (DeTraC-Decompose, Transfer and Compose) to project the high-dimension feature space into”
Artificial Intelligence (AI) and Big Data for Coronavirus (COVID-19) Pandemic: A Survey on the State-of-the-Arts (2020, methodology): “For example, the work in [46] leveraged a deep CNN model, called Decompose, Transfer, and Compose (DeTraC), to process chest X-ray images for the classification of COVID-19.”
Adoption of Digital Technologies in Health Care During the COVID-19 Pandemic: Systematic Review of Early Scientific Literature (2020, methodology): “In particular, within the reviewed articles, we identified numerous suggestions of the use of AI-powered tools for the diagnosis and screening of SARS-CoV-2 or COVID-19, as reported in Table 4 [20,22,23,25,26,28,30,32,33, 38,48,50,54,61,69,70,77,78,94,100,108,109,122,130].”
Exploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images (2021, positive): “[7], and can be useful in COVID-19 detection as well [8,9].”
Pneumonia Classification Using Deep Learning from Chest X-ray Images During COVID-19 (2021, positive): “…al. [45] With regards to the classification of COVID-19 and normal CXR images, it can be observed that our model provides significantly a better performance in comparison with studies that utilized a small number of datasets such as [42, 44, 46, 47] and models developed from scratch such as [29].”