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Transfer Learning and Semisupervised Adversarial Detection and Classification of COVID-19 in CT Images

Ariyo Oluwasanmi, Muhammad Umar Aftab, Zhiguang Qin, Son Tung Ngo, Thang Van Doan, Son Ba Nguyen, Son Hoang Nguyen, Dan Selisteanu
2021 Complexity  
With the advances in computer-aided diagnosis and artificial intelligence, this paper presents the application of deep learning and adversarial network for the automatic identification of COVID-19 pneumonia  ...  in computed tomography (CT) scans of the lungs.  ...  Conclusion is study presents a computer-aided analysis of COVID-19 CT scan images. e study analyzes the applicability of deep learning and convolutional neural network for the extraction of features in  ... 
doi:10.1155/2021/6680455 fatcat:vcmrzumcbjg25cqgf2bcf5mqey

Semi-supervised learning for an improved diagnosis of COVID-19 in CT images

Chang Hee Han, Misuk Kim, Jin Tae Kwak, Sandra Ortega-Martorell
2021 PLoS ONE  
The proposed method utilizes CT images in a supervised and unsupervised manner to improve the accuracy and robustness of COVID-19 diagnosis. Both labeled and unlabeled CT images are employed.  ...  Herein we propose a semi-supervised deep neural network for an improved detection of COVID-19.  ...  -19 CT images for an improved COVID-19 diagnosis.  ... 
doi:10.1371/journal.pone.0249450 pmid:33793650 fatcat:o55y3mqukjbbpblixnx2xhymxa

The role of imaging in the detection and management of COVID-19: a review

Di Dong, Zhenchao Tang, Shuo Wang, Hui Hui, Lixin Gong, Yao Lu, Zhong Xue, Hongen Liao, Fang Chen, Fan Yang, Ronghua Jin, Kun Wang (+7 others)
2020 IEEE Reviews in Biomedical Engineering  
Herein, we review the imaging characteristics and computing models that have been applied for the management of COVID-19.  ...  Lung infection or pneumonia is the common complication of COVID-19, and imaging techniques, especially computed tomography (CT), have played an important role in diagnosis and treatment assessment of the  ...  The latest Chinese diagnosis and treatment protocol for COVID-19 (trial version 7) also highlights the value of imaging for detecting COVID-19 [9] .  ... 
doi:10.1109/rbme.2020.2990959 pmid:32356760 fatcat:tsersvluzngkdntwockyqajre4

A Weakly-supervised Framework for COVID-19 Classification and Lesion Localization from Chest CT

Xinggang Wang, Xianbo Deng, Qing Fu, Qiang Zhou, Jiapei Feng, Hui Ma, Wenyu Liu, Chuansheng Zheng
2020 IEEE Transactions on Medical Imaging  
Developing a deep learning-based model for automatic COVID-19 diagnosis on chest CT is helpful to counter the outbreak of SARS-CoV-2.  ...  COVID-19 lesions are localized by combining the activation regions in the classification network and the unsupervised connected components. 499 CT volumes were used for training and 131 CT volumes were  ...  Thus, developing an artificial intelligence (AI) method for computer-aided COVID-19 diagnosis was very helpful to radiologists.  ... 
doi:10.1109/tmi.2020.2995965 pmid:33156775 fatcat:57l6554ztzfmjlsdgq6x2p5oq4

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.  ...  , as well as different pulmonary pathologies like airway diseases, lung cancer, COVID-19 and other infections.  ...  Medical imaging and COVID-19 Basically, the diagnosis of COVID-19 is based on polymerase chain reaction (PCR) tests, while the usage of medical imaging as a diagnostic test for COVID-19 was controversial  ... 
doi:10.1007/s00138-020-01101-5 pmid:32834523 pmcid:PMC7386599 fatcat:tkkylrptc5hkpoj52hjs3kuttu

COVID-view: Diagnosis of COVID-19 using Chest CT [article]

Shreeraj Jadhav, Gaofeng Deng, Marlene Zawin, Arie E. Kaufman
2021 arXiv   pre-print
scans for COVID-19, pulmonary embolism, and other forms of lung infections.  ...  Significant work has been done towards deep learning (DL) models for automatic lung and lesion segmentation and classification of COVID-19 on chest CT data.  ...  COVID-19 Chest-CT Imaging Features Ground Glass Opacity (GGO): GGO is the most common chest CT feature of COVID-19.  ... 
arXiv:2108.03799v1 fatcat:egk5kpzeprdrfbj42c47ylbjde

Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation and Diagnosis for COVID-19

Feng Shi, Jun Wang, Jun Shi, Ziyan Wu, Qian Wang, Zhenyu Tang, Kelei He, Yinghuan Shi, Dinggang Shen
2020 IEEE Reviews in Biomedical Engineering  
Moreover, the computer-aided platforms help radiologists make clinical decisions, i.e., for disease diagnosis, tracking, and prognosis.  ...  Medical imaging such as X-ray and computed tomography (CT) plays an essential role in the global fight against COVID-19, whereas the recently emerging artificial intelligence (AI) technologies further  ...  [52] assess longitudinal progression of COVID-19 by using voxel-level deep learning-based CT segmentation of pulmonary opacities. Huang et al.  ... 
doi:10.1109/rbme.2020.2987975 pmid:32305937 fatcat:cjswoasqh5b6hopdkcgceb5ca4

The Prominence of Artificial Intelligence in COVID-19 [article]

MD Abdullah Al Nasim, Aditi Dhali, Faria Afrin, Noshin Tasnim Zaman, Nazmul Karim
2021 arXiv   pre-print
Therefore, this survey paper explores the methodologies proposed that can aid doctors and researchers in early and inexpensive methods of diagnosis of the disease.  ...  On the other hand, the access to different types of medical images has motivated the researchers. As a result, a mammoth number of techniques are proposed.  ...  using computer-aided detection and diagnosis (CAD) systems.  ... 
arXiv:2111.09537v1 fatcat:repcv3sd4nccjijjtjjrsmggum

Artificial intelligence for rapid identification of the coronavirus disease 2019 (COVID-19) [article]

Xueyan Mei, Hao-Chih Lee, Kaiyue Diao, Mingqian Huang, Bin Lin, Chenyu Liu, Zongyu Xie, Yixuan Ma, Philip M. Robson, Michael Chung, Adam Bernheim, Venkatesh Mani (+16 others)
2020 biorxiv/medrxiv  
Chest computed tomography (CT) is a valuable component in the evaluation of patients with suspected SARS-CoV-2 infection.  ...  diagnosis of SARS-CoV-2 patients.  ...  Acknowledgements We thank the computational support by Biomedical Engineering and Imaging Institute at Icahn School of Medicine at Mount Sinai for this work.  ... 
doi:10.1101/2020.04.12.20062661 pmid:32511559 pmcid:PMC7274240 fatcat:hurnarx5tjhd5axrrjqquf6lwe

Learning from Pseudo Lesion: A Self-supervised Framework for COVID-19 Diagnosis [article]

Zhongliang Li, Zhihao Jin, Xuechen Li, Linlin Shen
2021 arXiv   pre-print
The Coronavirus disease 2019 (COVID-19) has rapidly spread all over the world since its first report in December 2019 and thoracic computed tomography (CT) has become one of the main tools for its diagnosis  ...  The pretrained encoder was then fine-tuned using labelled data for COVID-19 diagnosis task. Two public COVID-19 diagnosis datasets made up of CT images were employed for evaluation.  ...  GGO and Perlin noise 1) GGO: Although the final diagnosis of COVID-19 is based on RT-PCR, the early finding of abnormality in CT are vital for pneumonia detection [34] .  ... 
arXiv:2106.12313v1 fatcat:z2bhklbmprdadi7llrrwuewuqi

CovSeg-Unet: End-to-End Method-based Computer-Aided Decision Support System in Lung COVID-19 Detection on CT Images

Fatima Zahra EL BIACH, Imad IALA, Hicham LAANAYA, Khalid MINAOUI
2022 International Journal of Advanced Computer Science and Applications  
Thus, the implementation of rapid solutions for the early detection of this virus is an immediate priority.  ...  To this end, we provide a deep learning method called CovSeg-Unet to diagnose COVID-19 from chest CT images.  ...  In [17] , the authors proposed a computer-aided detection (CAD) method to assist radiologists to automatically detect COVID-19 on the chest X-ray images.  ... 
doi:10.14569/ijacsa.2022.0130162 fatcat:3gn62xponzctfdr47oztwzg3ia

An approach to categorize chest X-ray images using sparse categorical cross entropy

Chaithanya B. N., Swasthika Jain T. J., A. Usha Ruby, Ayesha Parveen
2021 Indonesian Journal of Electrical Engineering and Computer Science  
Reverse transcription-polymerase chain reaction (RT-PCR) testing, computed tomography (CT) scans, and chest X-ray (CXR) images are being used to identify Coronavirus, one of the most serious community  ...  The Coronavirus disease (COVID-19) pandemic is the most recent threat to global health.  ...  ACKNOWLEDGEMENTS The author wishes to express her gratitude to anonymous reviewers for their insightful remarks and ideas, which significantly improved the quality of this paper.  ... 
doi:10.11591/ijeecs.v24.i3.pp1700-1710 fatcat:mtwt3cxc6vcmvap476fcnhm544

Survey of the Detection and Classification of Pulmonary Lesions via CT and X-Ray [article]

Yixuan Sun, Chengyao Li, Qian Zhang, Aimin Zhou, Guixu Zhang
2020 arXiv   pre-print
This article reviews pulmonary CT and X-ray image detection and classification in the last decade.  ...  In recent years, the prevalence of several pulmonary diseases, especially the coronavirus disease 2019 (COVID-19) pandemic, has attracted worldwide attention.  ...  With the rapid development of artificial intelligence (AI), computer-aided diagnosis (CAD) has emerged as an effective method based on medical image analysis.  ... 
arXiv:2012.15442v1 fatcat:m6hwcww62res3pjzytmvglp7j4

COVID-XNet: A Custom Deep Learning System to Diagnose and Locate COVID-19 in Chest X-ray Images

Lourdes Duran-Lopez, Juan Pedro Dominguez-Morales, Jesús Corral-Jaime, Saturnino Vicente-Diaz, Alejandro Linares-Barranco
2020 Applied Sciences  
Medical imaging techniques, such as X-ray or chest computed tomography, are commonly used for this purpose due to their reliability for COVID-19 diagnosis.  ...  In this work, a novel Deep Learning-based system, called COVID-XNet, is presented for COVID-19 diagnosis in chest X-ray images.  ...  X-ray images for the COVID-19 class.  ... 
doi:10.3390/app10165683 fatcat:mt6haovizfcbpcdaosoiqwlnie

Coronavirus disease (COVID-19) cases analysis using machine-learning applications

Ameer Sardar Kwekha-Rashid, Heamn N Abduljabbar, Bilal Alhayani
2021 Applied Nanoscience  
The purpose of this study is to detect the role of machine-learning applications and algorithms in investigating and various purposes that deals with COVID-19.  ...  Review of the studies that had been published during 2020 and were related to this topic by seeking in Science Direct, Springer, Hindawi, and MDPI using COVID-19, machine learning, supervised learning,  ...  Declarations Conflict of interest There are no conflicts of interest.  ... 
doi:10.1007/s13204-021-01868-7 pmid:34036034 pmcid:PMC8138510 fatcat:u6lmwycdnjdovcnutrvj7rmzlu
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