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NTIS 바로가기Pattern recognition, v.121, 2022년, pp.108242 -
Guarrasi, Valerio (Unit of Computer Systems and Bioinformatics, Department of Engineering, University Campus Bio-Medico of Rome) , D’Amico, Natascha Claudia (Department of Diagnostic Imaging and Stereotactic Radiosurgery, Centro Diagnostico Italiano S.p.A.) , Sicilia, Rosa (Unit of Computer Systems and Bioinformatics, Department of Engineering, University Campus Bio-Medico of Rome) , Cordelli, Ermanno (Unit of Computer Systems and Bioinformatics, Department of Engineering, University Campus Bio-Medico of Rome) , Soda, Paolo (Unit of Computer Systems and Bioinformatics, Department of Engineering, University Campus Bio-Medico of Rome)
Abstract The year 2020 was characterized by the COVID-19 pandemic that has caused, by the end of March 2021, more than 2.5 million deaths worldwide. Since the beginning, besides the laboratory test, used as the gold standard, many applications have been applying deep learning algorithms to chest X-...
1 Tahamtan A. Real-time RT-PCR in COVID-19 detection: issues affecting the results Expert Rev. Mol. Diagn. 20 5 2020 453 454 32297805
2 Fang Y. Sensitivity of chest CT for COVID-19: comparison to RT-PCR Radiology 296 2 2020 E115 E117 32073353
3 Manna S. COVID-19: A multimodality review of radiologic techniques, clinical utility, and imaging features Radiology: Cardiothoracic Imaging 2 3 2020 e200210 33778588
4 W.H. Organisation, Use of chest imaging in COVID-19, (file:///C:/Users/00020626/Desktop/WHO-2019-nCoV-Clinical-Radiology_imaging-2020.1-eng.pdf), Online; accessed 31 March 2021.
5 Aljondi R. Diagnostic value of imaging modalities for COVID-19: scoping review J. Med. Internet Res. 22 8 2020 e19673 32716893
6 Wynants L. Prediction models for diagnosis and prognosis of COVID-19 infection: systematic review and critical appraisal Br Med J 369 2020
7 J.P. Cohen, et al., COVID-19 image data collection: Prospective predictions are the future, arXiv preprint arXiv:2006.11988(2020).
8 Hryniewska W. Checklist for responsible deep learning modeling of medical images based on COVID-19 detection studies Pattern Recognit 118 2021 108035 34054148
9 Cavalcanti G.D.C. Combining diversity measures for ensemble pruning Pattern Recognit Lett 74 2016 38 45
10 Gu J. Recent advances in convolutional neural networks Pattern Recognit 77 2018 354 377
11 Apostolopoulos I.D. COVID-19: Automatic detection from X-ray images utilizing transfer learning with convolutional neural networks Physical and Engineering Sciences in Medicine 2020 1
12 Kermany D.S. Identifying medical diagnoses and treatable diseases by image-based deep learning Cell 172 5 2018 1122 1131 29474911
13 Loey M. Within the lack of chest COVID-19 X-ray dataset: a novel detection model based on GAN and deep transfer learning Symmetry (Basel) 12 4 2020 651
14 Rahimzadeh M. A modified deep convolutional neural network for detecting COVID-19 and pneumonia from chest X-ray images based on the concatenation of xception and resnet50v2 Informatics in Medicine Unlocked 2020 100360 32501424
15 Wang X. ChestX-Ray8: Hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases Proceedings of the IEEE Conf. on computer vision and pattern recognition 2017 2097 2106
16 Vaid S. Deep learning COVID-19 detection bias: accuracy through artificial intelligence Int Orthop 2020 1 31834443
17 Ozturk T. Automated detection of COVID-19 cases using deep neural networks with X-ray images Comput. Biol. Med. 2020 103792 32568675
18 Toğaçar M. COVID-19 Detection using deep learning models to exploit social mimic optimization and structured chest X-ray images using fuzzy color and stacking approaches Comput. Biol. Med. 2020 103805 32568679
19 Chowdhury M.E.H. Can AI help in screening viral and COVID-19 pneumonia? IEEE Access 8 2020 132665 132676
20 Wang Z. Automatically discriminating and localizing COVID-19 from community-acquired pneumonia on chest X-rays Pattern Recognit 110 2021 107613 32868956
21 Brunese L. Explainable deep learning for pulmonary disease and coronavirus COVID-19 detection from X-rays Comput Methods Programs Biomed 196 2020 105608 32599338
22 Khan A.I. CoroNet: a deep neural network for detection and diagnosis of COVID-19 from chest X-ray images Comput Methods Programs Biomed 2020 105581 32534344
23 Wang Z. Automatically discriminating and localizing COVID-19 from community-acquired pneumonia on chest x-rays Pattern Recognit 110 2021 107613 32868956
24 Afshar P. COVID-CAPS: A capsule network-based framework for identification of COVID-19 cases from X-ray images Pattern Recognit Lett 138 2020 638 643 32958971
25 Shorfuzzaman M. Metacovid: a siamese neural network framework with contrastive loss for n-shot diagnosis of COVID-19 patients Pattern Recognit 113 2021 107700 33100403
26 Wang L. COVID-Net: A tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images Sci Rep 10 1 2020 1 12 31913322
27 Li J. Multi-task contrastive learning for automatic CT and X-ray diagnosis of COVID-19 Pattern Recognit 114 2021 107848 33518812
28 Fan Y. COVID-19 detection from X-ray images using multi-kernel-size spatial-channel attention network Pattern Recognit 2021 108055 34103766
29 Vieira P. Detecting pulmonary diseases using deep features in X-ray images Pattern Recognit 2021 108081 34149099
30 Desai S. Chest imaging representing a COVID-19 positive rural US population Sci Data 7 1 2020 1 6 31896794
31 P. Soda, et al., AIforCOVID: predicting the clinical outcomes in patients with COVID-19 applying AI to chest-X-rays. an italian multicentre study, arXiv preprint arXiv:2012.06531(2020).
32 Signoroni A. BS-Net: Learning COVID-19 pneumonia severity on a large chest X-ray dataset Med Image Anal 71 2021 102046 33862337
33 Huang S.C. Fusion of medical imaging and electronic health records using deep learning: a systematic review and implementation guidelines NPJ digital medicine 3 1 2020 1 9 31934645
34 Baltrušaitis T. Multimodal machine learning: a survey and taxonomy IEEE Trans Pattern Anal Mach Intell 41 2 2018 423 443 29994351
35 Kuncheva L.I. A theoretical study on six classifier fusion strategies IEEE Trans Pattern Anal Mach Intell 24 2 2002 281 286
36 Dietterich T.G. An experimental comparison of three methods for constructing ensembles of decision trees: bagging, boosting, and randomization Mach Learn 40 2 2000 139 157
37 Ronneberger O. U-Net: Convolutional networks for biomedical image segmentation International Conf. on Medical image computing and computer-assisted intervention 2015 Springer 234 241
38 Jaeger S. Two public chest X-ray datasets for computer-aided screening of pulmonary diseases Quant Imaging Med Surg 4 6 2014 475 25525580
39 Shiraishi J. Development of a digital image database for chest radiographs with and without a lung nodule: receiver operating characteristic analysis of radiologists’ detection of pulmonary nodules American Journal of Roentgenology 174 1 2000 71 74 10628457
40 Rawat W. Deep convolutional neural networks for image classification: a comprehensive review Neural Comput 29 9 2017 2352 2449 28599112
41 Brown G. Diversity creation methods: a survey and categorisation Information fusion 6 1 2005 5 20
42 John F. Extremum Problems with Inequalities as Subsidiary Conditions Traces and emergence of nonlinear programming 2014 Springer 197 215
43 Kuhn H.W. Nonlinear Programming Traces and emergence of nonlinear programming 2014 Springer 247 258
44 A. Krizhevsky, One weird trick for parallelizing convolutional neural networks, arXiv preprint arXiv:1404.5997(2014).
45 K. Simonyan, et al., Very deep convolutional networks for large-scale image recognition, arXiv preprint arXiv:1409.1556(2014).
46 Szegedy C. Going deeper with convolutions Proceedings of the IEEE Conf. on computer vision and pattern recognition 2015 1 9
47 He K. Deep residual learning for image recognition Proceedings of the IEEE Conf. on computer vision and pattern recognition 2016 770 778
48 S. Zagoruyko, et al., Wide residual networks, arXiv preprint arXiv:1605.07146(2016).
49 Xie S. Aggregated residual transformations for deep neural networks Proceedings of the IEEE Conf. on computer vision and pattern recognition 2017 1492 1500
50 F.N. Iandola, et al., SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5 MB model size, arXiv preprint arXiv:1602.07360(2016).
51 Huang G. Densely connected convolutional networks Proceedings of the IEEE Conf. on computer vision and pattern recognition 2017 4700 4708
52 Sandler M. MobileNetV2: Inverted residuals and linear bottlenecks Proceedings of the IEEE Conf. on computer vision and pattern recognition 2018 4510 4520
53 Deng J. ImageNet: A large-scale hierarchical image database 2009 IEEE Conf. on computer vision and pattern recognition 2009 IEEE 248 255
54 Hinton G.E. Matrix capsules with EM routing International conference on learning representations 2018
55 Das D. Truncated inception net: COVID-19 outbreak screening using chest X-rays Physical and engineering sciences in medicine 43 3 2020 915 925 32588200
56 Selvaraju R.R. Grad-CAM: Visual explanations from deep networks via gradient-based localization Proceedings of the IEEE international Conf. on computer vision 2017 618 626
57 Basu S. Deep learning for screening covid-19 using chest x-ray images 2020 IEEE Symposium Series on Computational Intelligence (SSCI) 2020 IEEE 2521 2527
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