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Chest Radiographs Images Retrieval Using Deep Learning Networks

Sawsan M. Mahmoud, Hanan A. S. Al-Jubouri, Tawfeeq E. Abdoulabbas
2022 Bulletin of Electrical Engineering and Informatics  
COVID-19 (–)) for X-ray and CT-scan respectively.  ...  Content-based image retrieval in terms of medical images offers such a facility based on visual feature descriptor and similarity measurements.  ...  ACKNOWLEDGEMENTS The authors of this work would like to thank Mustansiriyah University http://www.uomustansiriyah.edu.iq in Baghdad, Iraq for its support.  ... 
doi:10.11591/eei.v11i3.3478 fatcat:kgpitveycfa5hk4in6pv6jav7e

Deep Learning applications for COVID-19

Connor Shorten, Taghi M. Khoshgoftaar, Borko Furht
2021 Journal of Big Data  
Deep Learning has additionally been utilized in Spread Forecasting for Epidemiology. Our literature review has found many examples of Deep Learning systems to fight COVID-19.  ...  We begin by evaluating the current state of Deep Learning and conclude with key limitations of Deep Learning for COVID-19 applications.  ...  Acknowledgements We would like to thank the reviewers in the Data Mining and Machine Learning Laboratory at Florida Atlantic University.  ... 
doi:10.1186/s40537-020-00392-9 pmid:33457181 pmcid:PMC7797891 fatcat:aokxo63z2rhdpfxo3egyf3xpcm

Is Medical Chest X-ray Data Anonymous? [article]

Kai Packhäuser, Sebastian Gündel, Nicolas Münster, Christopher Syben, Vincent Christlein, Andreas Maier
2021 arXiv   pre-print
Furthermore, we achieve an AUC of up to 0.9870 and a precision@1 of up to 0.9444 when evaluating our trained networks on CheXpert and the COVID-19 Image Data Collection.  ...  To the best of our knowledge, we are the first to show that a well-trained deep learning system is able to recover the patient identity from chest X-ray data.  ...  Acknowledgements The research leading to these results has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (ERC grant no  ... 
arXiv:2103.08562v3 fatcat:6snwohaamvgqlokdpov5dn3ggq

COVID-19-CT-CXR: a freely accessible and weakly labeled chest X-ray and CT image collection on COVID-19 from biomedical literature

Yifan Peng, Yuxing Tang, Sungwon Lee, Yingying Zhu, Ronald Summers, Zhiyong Lu
2020 IEEE Transactions on Big Data  
Because a large portion of figures in COVID-19 articles are not CXR or CT, we designed a deep-learning model to distinguish them from other figure types and to classify them accordingly.  ...  DL) performance for the classification of COVID-19 and non-COVID-19 CT. (2) We collected CT images of influenza, another common infectious respiratory illness that may present similarly to COVID-19, and  ...  These articles contain rich chest radiographs and CT images that are helpful for scientists and clinicians in describing COVID-19 cases.  ... 
doi:10.1109/tbdata.2020.3035935 pmid:33997112 pmcid:PMC8117951 fatcat:kk25kejyo5bzlhlhk3dbbr6rda

Fully automatic deep convolutional approaches for the analysis of Covid-19 using chest X-ray images [article]

Joaquim de Moura, Jorge Novo, Marcos Ortega
2020 medRxiv   pre-print
Among its applications, chest X-ray images are frequently used for an early diagnostic/screening of Covid-19 disease, given the frequent pulmonary impact in the patients, critical issue to prevent further  ...  In this work, we propose complementary fully automatic approaches for the classification of chest X-ray images under the analysis of 3 different categories: Covid-19, pneumonia and healthy cases.  ...  Acknowledgement This work is supported by the Instituto de Salud Carlos III, Government of Spain and FEDER funds of the European Union through the DTS18/00136 research projects and by the Ministerio de  ... 
doi:10.1101/2020.05.01.20087254 fatcat:is2mjeoi6rdsdi2mr7ga7wezmi

COVID-19-CT-CXR: a freely accessible and weakly labeled chest X-ray and CT image collection on COVID-19 from biomedical literature [article]

Yifan Peng, Yu-Xing Tang, Sungwon Lee, Yingying Zhu, Ronald M. Summers, Zhiyong Lu
2020 arXiv   pre-print
compared clinical symptoms and clinical findings of COVID-19 vs. those of influenza to demonstrate the disease differences in the scientific publications.  ...  for the classification of COVID-19 and non-COVID-19 CT. (2) We collected CT images of influenza and trained a DL baseline to distinguish a diagnosis of COVID-19, influenza, or normal or other types of  ...  Acknowledgment This research was supported in part by the Intramural Research Programs of the National Library of Medicine (NLM) and National Institutes of Health (NIH) Clinical Center.  ... 
arXiv:2006.06177v2 fatcat:wjs7vci25zgx5mimtpnliqa3wu

A Review on Deep Learning Techniques for the Diagnosis of Novel Coronavirus (COVID-19)

Md. Milon Islam, Fakhri Karray, Reda Alhajj, Jia Zeng
2021 IEEE Access  
This paper aims to overview the recently developed systems based on deep learning techniques using different medical imaging modalities like Computer Tomography (CT) and X-ray.  ...  This review specifically discusses the systems developed for COVID-19 diagnosis using deep learning techniques and provides insights on well-known data sets used to train these networks.  ...  Class imbalance is another big issue for deep learning based COVID-19 diagnosis systems. Data related to COVID-19 exist far less than other common lung diseases in chest X-ray and CT images.  ... 
doi:10.1109/access.2021.3058537 pmid:34976571 pmcid:PMC8675557 fatcat:d52pbcisz5gufdew4bjwiex7ca

Study of Different Deep Learning Methods for Coronavirus (COVID-19) Pandemic: Taxonomy, Survey and Insights

Lamia Awassa, Imen Jdey, Habib Dhahri, Ghazala Hcini, Awais Mahmood, Esam Othman, Muhammad Haneef
2022 Sensors  
This research seeks to provide an overview of novel deep learning-based applications for medical imaging modalities, computer tomography (CT) and chest X-rays (CXR), for the detection and classification  ...  Then, utilizing deep learning techniques, we present an overview of systems created for COVID-19 detection and classification.  ...  Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable.  ... 
doi:10.3390/s22051890 pmid:35271037 pmcid:PMC8915023 fatcat:yzso2egndjd63ec2ykqppub7tm

A Review on Deep Learning Techniques for the Diagnosis of Novel Coronavirus (COVID-19) [article]

Md. Milon Islam, Fakhri Karray, Reda Alhajj, Jia Zeng
2020 arXiv   pre-print
This paper aims to overview the recently developed systems based on deep learning techniques using different medical imaging modalities like Computer Tomography (CT) and X-ray.  ...  This review specifically discusses the systems developed for COVID-19 diagnosis using deep learning techniques and provides insights on well-known data sets used to train these networks.  ...  A total of 339,271 images were taken where 144 images for COVID-19 patients, 108,948 samples of pneumonia and bacteria except COVID-19, 224,316 chest radiographs of bacteria and pneumonia, and 5,863 paediatric  ... 
arXiv:2008.04815v1 fatcat:zxfzqyq6hvhhhdgj3jdqapbc5q

Classification Framework for COVID-19 Diagnosis Based on Deep CNN Models

Walid El-Shafai, Abeer D. Algarni, Ghada M. El Banby, Fathi E. Abd El-Samie, Naglaa F. Soliman
2022 Intelligent Automation and Soft Computing  
This paper introduces a DCNN (Deep Convolutional Neural Network) framework for chest X-ray and CT image classification based on TL (Transfer Learning).  ...  COVID-19 first appeared in China, Wuhan, and then it has exploded in the whole world with a very bad impact on our daily life.  ...  The need to interpret radiographic images has inspired a series of deep learning AI systems [3] , which have shown promising accuracy results in detecting COVID-19 cases from radiographic imagery [4,  ... 
doi:10.32604/iasc.2022.020386 fatcat:nxt3egdlcjchrnhnij5evvjsju

Deep learning applications in pulmonary medical imaging: recent updates and insights on COVID-19

Hanan Farhat, George E. Sakr, Rima Kilany
2020 Machine Vision and Applications  
This paper reviews the development of deep learning applications in medical image analysis targeting pulmonary imaging and giving insights of contributions to COVID-19.  ...  Likewise, deep learning applications (DL) on pulmonary medical images emerged to achieve remarkable advances leading to promising clinical trials.  ...  Deep learning applications to COVID-19 medical imaging analysis Contributions to deep learning applications for COVID-19 that were published in pre-prints until the end of May 2020 were excluded from the  ... 
doi:10.1007/s00138-020-01101-5 pmid:32834523 pmcid:PMC7386599 fatcat:tkkylrptc5hkpoj52hjs3kuttu

COVID-19 Severity Classification on Chest X-ray Images [article]

Aditi Sagar, Aman Swaraj, Karan Verma
2022 arXiv   pre-print
In this work, we classify covid images based on the severity of the infection. First, we pre-process the X-ray images using a median filter and histogram equalization.  ...  Biomedical imaging analysis combined with artificial intelligence (AI) methods has proven to be quite valuable in order to diagnose COVID-19.  ...  Abinash Mishra, MD, (Government Hospital; Odisha, India) for classifying the lungs images as per their severity.  ... 
arXiv:2205.12705v1 fatcat:fky325h5qff75i7n3kztru2p6y

Prediction of Patient Management in COVID-19 Using Deep Learning-Based Fully Automated Extraction of Cardiothoracic CT Metrics and Laboratory Findings

Thomas Weikert, Saikiran Rapaka, Sasa Grbic, Thomas Re, Shikha Chaganti, David J. Winkel, Constantin Anastasopoulos, Tilo Niemann, Benedikt J. Wiggli, Jens Bremerich, Raphael Twerenbold, Gregor Sommer (+2 others)
2021 Korean Journal of Radiology  
To extract pulmonary and cardiovascular metrics from chest CTs of patients with coronavirus disease 2019 (COVID-19) using a fully automated deep learning-based approach and assess their potential to predict  ...  Multiple pulmonary and cardiovascular metrics were extracted from the chest CT images using deep learning.  ...  Additionally, we appreciate the great support of our research team, namely Rita Achermann, Ivan Nesic, Joshy Cyriac, and Bram Stieltjes.  ... 
doi:10.3348/kjr.2020.0994 pmid:33686818 pmcid:PMC8154782 fatcat:5cbqsomtsnd7dkojsag74al3uu

The Progress of Medical Image Semantic Segmentation Methods for Application in COVID-19 Detection

Amin Valizadeh, Morteza Shariatee, Suresh Manic
2021 Computational Intelligence and Neuroscience  
The traditional method and the published dataset for segmentation are reviewed in the first step.  ...  , multistage and multifeature methods, supervised methods, semiregulatory methods, and nonregulatory methods, are then thoroughly explored in current methods based on the deep neural network.  ...  for publication.  ... 
doi:10.1155/2021/7265644 pmid:34840563 pmcid:PMC8611358 fatcat:6xsjrgiabjat5c6uinwa2kbyay

Role of Deep Learning in Early Detection of COVID-19: Scoping Review

Mahmood Alzubaidi, Haider Dhia Zubaydi, Ali Bin-Salem, Alaa A Abd-Alrazaq, Arfan Ahmed, Mowafa Househ
2021 Computer Methods and Programs in Biomedicine Update  
All studies used deep learning for detection of COVID-19 cases in early stage based on different diagnostic modalities.  ...  Deep Learning (DL) is a branch of Artificial intelligence (AI) applications, the recent growth of DL includes features that could be helpful in fighting the COVID-19 pandemic.  ...  learning, artificial intelligence and deep learning" and the target disease "Coronavirus and COVID-19". total retrieved studies in (Appendix A). .  ... 
doi:10.1016/j.cmpbup.2021.100025 pmid:34345877 pmcid:PMC8321699 fatcat:vdvd47unpvaslo3ie2oofyxbfa
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