RETRACTED ARTICLE: Deep learning system to screen coronavirus disease 2019 pneumonia

By C. Butt, Jagpal Gill, David Chun, B. Babu. Applied intelligence (Boston), 2020

638 citations recorded by the source corpus.

The Editor in Chief has retracted this article (1) because the data published here was previously presented by a different set of authors at arXiv pre-print server (2) and a large amount of text, some figures and tables have been re-used without appropriate citation or acknowledgment. The pre-print has been published (3). Author Benson Babu agrees to this retraction. Authors Charmine Butt, Jagpal Gill and David Chun did not respond to any correspondence regarding this retraction. © Springer Science+Business Media, LLC, part of Springer Nature 2020.

Subfields: Critical Care and Intensive Care Medicine, Oncology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/35751010cf4b09552ed4c85b5a1367f96db2641f

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Citations in the corpus

Inf-Net: Automatic COVID-19 Lung Infection Segmentation From CT Images (2020, methodology): “In this work, we focus on patient scale applications [18] , [22] , [37] , [38] , [41] - [44] , especially those based on CT scans.”
Radiologist-Level COVID-19 Detection Using CT Scans with Detail-Oriented Capsule Networks (2020, methodology): “[13] proposed an architecture based on ResNet18 to extract features from CT scans, segment out pathological lung regions which then serve as the input to a classifier for predict COVID-19 disease.”
AI-assisted CT imaging analysis for COVID-19 screening: Building and deploying a medical AI system (2020, positive): “Taking some approaches for instance, Xu et al. [48] build a deep learning model based on V-Net [14] and ResNet-18 to classify COVID-19 patients, Influenza-A patients, and healthy people.”
Artificial Intelligence and COVID-19: Deep Learning Approaches for Diagnosis and Treatment (2020, methodology): “To realize a classification network that facilitates discrimination between COVID-19 and Influenza-A viral pneumonia, a DL technology was used for network structure, and the classical ResNet was used to extract features [24].”
Coronavirus (Covid-19) Classification Using CT Images by Machine Learning Methods (2020, neutral): “[6] 3D-deep learning CT, 618 images 3 87.”
COVIDiagnosis-Net: Deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images (2020, neutral): “The study [14] indicates a three-class model that can distinguish the COVID-19, Influenza-A viral-based pneumonia, and healthy cases.”
Improving performance of CNN to predict likelihood of COVID-19 using chest X-ray images with preprocessing algorithms (2020, methodology): “Butt et al. in [9] chose a local-attention mechanism model for image feature extraction, and ResNet as a classical CNN system to classify chest CT images depicting COVID-19 with an overall accuracy of 86.7%.”
COVID-ResNet: A Deep Learning Framework for Screening of COVID19 from Radiographs (2020, background): “Some studies have shown the use of imaging techniques such as X-rays or Computed Tomography (CT-scans) for finding characteristic symptoms of the novel corona virus in these imaging techniques [2], [3].”
Automated detection of COVID-19 cases using deep neural networks with X-ray images (2020, methodology): “[49] achieved an 86.7% performance rate in detecting COVID-19 using ResNet coupled with CT images.”
COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images (2020, background): “Motivated by the need for faster interpretation of radiography images, a number of artificial intelligence (AI) systems based on deep learning [8] have been proposed and results have shown to be quite promising in terms of accuracy in detecting patients infected with COVID-19 via radiography imaging”
Deep learning and medical image processing for coronavirus (COVID-19) pandemic: A survey (2020, positive): “The work in Xu et al. (2020) found pangolins as a latent intermediate host of coronavirus.”
The Role of Imaging in the Detection and Management of COVID-19: A Review (2020, background): “Since human-annotatedROIscanbeaffectedbydifferentusersand aretime-consuming,Xu et al. [73] proposed a deep-learning model to automatically detect infectious areas in CT images, and then used 3DResNet to identify whether the infectious areas were COVID-19.”
Dual-Sampling Attention Network for Diagnosis of COVID-19 From Community Acquired Pneumonia (2020, methodology): “[40] use a V-Net model to segment the infection region and apply a ResNet18 network for the classification.”