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Automatic Distinction between COVID-19 and Common Pneumonia using Multi-Scale Convolutional Neural Network on Chest CT Scans

Tao Yan, Pak Kin Wong, Hao Ren, Huaqiao Wang, Jiangtao Wang, Yang Li
2020 Chaos, Solitons & Fractals  
Based on these CT scans, we design an artificial intelligence (AI) system that uses a multi-scale convolutional neural network (MSCNN) and evaluate its performance at both slice level and scan level.  ...  Experimental results show that the proposed AI has promising diagnostic performance in the detection of COVID-19 and differentiating it from other common pneumonia under limited number of training data  ...  Chi Hong Wong for his helpful discussion and proofreading of the manuscript. This work was funded by The Science and Technology Development Fund, Macau SAR (File no. 0021/2019/A).  ... 
doi:10.1016/j.chaos.2020.110153 pmid:32834641 pmcid:PMC7381895 fatcat:dbh2xkpnfnew7erdiwwywnnibu

Deep learning for differentiating novel coronavirus pneumonia and influenza pneumonia

Min Zhou, Dexiang Yang, Yong Chen, Yanping Xu, Jin-Fu Xu, Zhijun Jie, Weiwu Yao, Xiaoyan Jin, Zilai Pan, Jingwen Tan, Lan Wang, Yihan Xia (+8 others)
2021 Annals of Translational Medicine  
In this study, we aimed to develop and validate an integrated deep learning framework on chest CT images for the automatic detection of NCP, focusing particularly on differentiating NCP from influenza  ...  Of the NCP lesions, 96.6% were >1 cm and 76.8% were of a density <-500 Hu, indicating them to have less consolidation than IP lesions, which had nodules ranging from 5-10 mm.  ...  which permits the noncommercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the  ... 
doi:10.21037/atm-20-5328 pmid:33569413 pmcid:PMC7867927 fatcat:bbsdsj3egraj3i7xtpcf7zdizi

Intelligent Pneumonia Identification from Chest X-Rays: A Systematic Literature Review [article]

Wasif Khan, Nazar Zaki, Luqman Ali
2020 medRxiv   pre-print
Various automated systems have been proposed for the rapid detection of pneumonia on chest x-rays images Although such detection algorithms are many and varied, they have not been summarized into a review  ...  It also discusses the quality, usability, and size of the available datasets, and ways of coping with unbalanced datasets.  ...  They collected three datasets of CXR images and checked the pulmonary detection performances of their method using multiple classifiers (kneural network, SVM, and naïve Bayes).  ... 
doi:10.1101/2020.07.09.20150342 fatcat:kuhmml67x5erfgtxf2kpq6fnyu

M3Lung-Sys: A Deep Learning System for Multi-Class Lung Pneumonia Screening from CT Imaging [article]

Xuelin Qian, Huazhu Fu, Weiya Shi, Tao Chen, Yanwei Fu, Fei Shan, Xiangyang Xue
2020 arXiv   pre-print
Considering the limited training cases and resources (e.g, time and budget), we propose a Multi-task Multi-slice Deep Learning System (M3Lung-Sys) for multi-class lung pneumonia screening from CT imaging  ...  , which only consists of two 2D CNN networks, i.e., slice- and patient-level classification networks.  ...  ACKNOWLEDGMENT The authors would like to thank all of clinicians, patients and researchers who gave valuable time effort and support for this project, especially in data collection and annotation Additionally  ... 
arXiv:2010.03201v1 fatcat:ye22zi5hejddzdizfsy2ydhaui

COVID-19 Pneumonia Classification Based on NeuroWavelet Capsule Network

Happy Nkanta Monday, Jianping Li, Grace Ugochi Nneji, Saifun Nahar, Md Altab Hossin, Jehoiada Jackson
2022 Healthcare  
We examined the proposed model on a public-sourced dataset of pneumonia-related illnesses, including COVID-19 confirmed cases and healthy CXR images.  ...  robustness of the network.  ...  Materials and Methods Wavelet Wavelets are a type of function used to scale and localize functions.  ... 
doi:10.3390/healthcare10030422 pmid:35326900 pmcid:PMC8949056 fatcat:ih5dozzdircjrjqwid2zrill6a

Advancement of Deep Learning in Pneumonia and Covid-19 Classification and Localization: A Qualitative and Quantitative Analysis [article]

Aakash Shah, Manan Shah
2021 arXiv   pre-print
With the help of Deep Learning models, pneumonia and covid-19 can be detected instantly from Chest X-rays or CT scans.  ...  By compiling and analyzing a large quantum of research details in one place with all the datasets, model architectures, and results, we aim to provide a one-stop solution to beginners and current researchers  ...  They also presented a multi-objective algorithm to automatically optimize hyperparameters while training.  ... 
arXiv:2111.08606v1 fatcat:oqjgceqhtfaghkhojmsoqwzwrq

Automatically discriminating and localizing COVID-19 from community-acquired pneumonia on chest X-rays

Zheng Wang, Ying Xiao, Yong Li, Jie Zhang, Fanggen Lu, Muzhou Hou, Xiaowei Liu
2020 Pattern Recognition  
A retrospective chest X-ray image dataset was collected from open image data and the Xiangya Hospital, which was divided into a training group and a testing group.  ...  It is an immediate priority to develop and test a computer-aided detection (CAD) scheme based on deep learning (DL) to automatically localize and differentiate COVID-19 from community-acquired pneumonia  ...  The Localization-DL constructed attention modules use a state-of-the-art residual attention network basic unit.  ... 
doi:10.1016/j.patcog.2020.107613 pmid:32868956 pmcid:PMC7448783 fatcat:t3w4uh3ezzfclpu77d3m6kxvjy

$M^3$Lung-Sys: A Deep Learning System for Multi-Class Lung Pneumonia Screening from CT Imaging

Xuelin Qian, Huazhu Fu, Weiya Shi, Tao Chen, Yanwei Fu, Fei Shan, Xiangyang Xue
2020 IEEE journal of biomedical and health informatics  
Considering the limited training cases and resources (e.g, time and budget), we propose a Multi-task Multi-slice Deep Learning System ( M3Lung-Sys) for multi-class lung pneumonia screening from CT imaging  ...  , which only consists of two 2D CNN networks, i.e., slice- and patient-level classification networks.  ...  ACKNOWLEDGMENT The authors would like to thank all of clinicians, patients and researchers who gave valuable time effort and support for this project, especially in data collection and annotation Additionally  ... 
doi:10.1109/jbhi.2020.3030853 pmid:33048773 fatcat:yuvw4fqwkrbvxo6gxwtzzblfq4

Novel coronavirus pneumonia detection and segmentation based on the deep-learning method

Zhiliang Zhang, Xinye Ni, Guanying Huo, Qingwu Li, Fei Qi
2021 Annals of Translational Medicine  
A novel method to detect and segment coronavirus pneumonia was established based on the deep-learning algorithm.  ...  The experimental results showed that our method achieved start-of-the-art performance on the pneumonia dataset.  ...  U-net network is a kind of full convolution semantic segmentation network model, which uses a symmetric encoding and decoding structure, and can effectively capture multi-scale targets and optimize the  ... 
doi:10.21037/atm-21-1156 pmid:34350249 pmcid:PMC8263886 fatcat:hm45aauda5eljiog6lrg74yjea

Deep Learning Applied to Chest Radiograph Classification—A COVID-19 Pneumonia Experience

Adhvan Furtado, Leandro Andrade, Diego Frias, Thiago Maia, Roberto Badaró, Erick G. Sperandio Nascimento
2022 Applied Sciences  
Due to the recent COVID-19 pandemic, a large number of reports present deep learning algorithms that support the detection of pneumonia caused by COVID-19 in chest radiographs.  ...  The Cimatec_XCOV19 algorithm obtained a sensitivity of 0.85, specificity of 0.82, and AUC ROC of 0.93.  ...  Acknowledgments: We gratefully acknowledge the support of SENAI CIMATEC AI Reference Center and the SENAI CIMATEC/NVDIA AI Joint Center for scientific and technical support, the SENAI CIMATEC Supercomputing  ... 
doi:10.3390/app12083712 fatcat:lju5rl5bgfbihaghldf4d4qrm4

Visualization and Interpretation of Convolutional Neural Network Predictions in Detecting Pneumonia in Pediatric Chest Radiographs

Sivaramakrishnan Rajaraman, Sema Candemir, Incheol Kim, George Thoma, Sameer Antani
2018 Applied Sciences  
In this study, we evaluate, visualize, and explain the performance of customized CNNs to detect pneumonia and further differentiate between bacterial and viral types in pediatric CXRs.  ...  We observe that the customized VGG16 model achieves 96.2% and 93.6% accuracy in detecting the disease and distinguishing between bacterial and viral pneumonia respectively.  ...  Configuring CNNs for Pneumonia Detection We evaluated the performance of different customized CNNs and a VGG16 model in detecting pneumonia and furthermore distinguishing between bacterial and viral types  ... 
doi:10.3390/app8101715 pmid:32457819 pmcid:PMC7250407 fatcat:orvbggc3dnfsphaa34jtxwtffy

FedDPGAN: Federated Differentially Private Generative Adversarial Networks Framework for the Detection of COVID-19 Pneumonia

Longling Zhang, Bochen Shen, Ahmed Barnawi, Shan Xi, Neeraj Kumar, Yi Wu
2021 Information Systems Frontiers  
Under Independent and Identically Distributed (IID) and non-IID settings, the evaluation of the proposed model is on three types of chest X-ray (CXR)images dataset (COVID-19, normal, and normal pneumonia  ...  Under the FL framework and Differentially Private thinking, we propose a Federated Differentially Private Generative Adversarial Network (FedDPGAN) to detect COVID-19 pneumonia for sustainable smart cities  ...  Specifically, such a dataset contains 2,000 normal images, 1,250 normal pneumonia images and 350 COVID-19 pneumonia images.  ... 
doi:10.1007/s10796-021-10144-6 pmid:34149305 pmcid:PMC8204125 fatcat:4ex2m46q35g3zflerhceal2abi

COVID-GATNet: A Deep Learning Framework for Screening of COVID-19 from Chest X-Ray Images

Junfeng Li, Dehai Zhang, Qing Liu, Rongjing Bu, Qi Wei
2020 2020 IEEE 6th International Conference on Computer and Communications (ICCC)  
This study introduces a new neural network model, COVID-GATNet, to assist radiologists in automatically diagnosing CXR images, thus improving the detection speed of suspected infected people.  ...  Because there is less open-source data for COVID-19 positive CXR images than the other two types of data, this research dilated CODIV-19 positive CXR images by scaling, rotating, adjusting brightness and  ...  COVID-GATNet adds multiple sets of independent attention mechanisms by adding a graph attention layer after feature extraction so that the multi-head attention mechanism can place the attention distribution  ... 
doi:10.1109/iccc51575.2020.9345005 fatcat:wfozoppuq5fnva2ojbde5wrsme

AI-Driven CT-based quantification, staging and short-term outcome prediction of COVID-19 pneumonia [article]

Guillaume Chassagnon, Maria Vakalopoulou, Enzo Battistella, Stergios Christodoulidis, Trieu-Nghi Hoang-Thi, Severine Dangeard, Eric Deutsch, Fabrice Andre, Enora Guillo, Nara Halm, Stefany El Hajj, Florian Bompard (+19 others)
2020 arXiv   pre-print
In this multi-centric study, we propose an end-to-end artificial intelligence solution for automatic quantification and prognosis assessment by combining automatic CT delineation of lung disease meeting  ...  Chest computed tomography (CT) is widely used for the management of Coronavirus disease 2019 (COVID-19) pneumonia because of its availability and rapidity.  ...  Detection of sars-cov-2 in different types of clinical specimens. Jama (2020). 29. CDC.  ... 
arXiv:2004.12852v1 fatcat:zrlolg4lpjb3tavvknncsa7jze

Production of d-lactate from glucose using Klebsiella pneumoniae mutants

Xinjun Feng, Liqun Jiang, Xiaojuan Han, Xiutao Liu, Zhiqiang Zhao, Huizhou Liu, Mo Xian, Guang Zhao
2017 Microbial Cell Factories  
However, only a few studies have focused on K. pneumoniae for d-lactate production with a significant amount of by-products, which complicated the purification process and decreased the yield of d-lactate  ...  Conclusions: Knocking out by-product synthesis genes had a remarkable influence on the production and yield of d-lactate.  ...  Roy Curtiss III (Arizona State University) for strain χ7213 and pRE112.  ... 
doi:10.1186/s12934-017-0822-6 pmid:29162110 pmcid:PMC5697408 fatcat:2x2csm7eybgnbn6bj6kbi6ieju
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