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Current Medical Imaging

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ISSN (Print): 1573-4056
ISSN (Online): 1875-6603

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Deep Learning Based COVID-19 Detection Using Medical Images: Is Insufficient Data Handled Well?

Author(s): Caren Babu, Rahul Manohar O and D Abraham Chandy*

Volume 19, Issue 4, 2023

Published on: 29 August, 2022

Article ID: e030822207243 Pages: 5

DOI: 10.2174/1573405618666220803123626

Abstract

Deep learning is a prominent method for automatic detection of COVID-19 disease using a medical dataset. This paper aims to give a perspective on the data insufficiency issue that exists in COVID-19 detection associated with deep learning. The extensive study of the available datasets comprising CT and X-ray images is presented in this paper, which can be very much useful in the context of a deep learning framework for COVID-19 detection. Moreover, various data handling techniques that are very essential in deep learning models are discussed in detail. Advanced data handling techniques and approaches to modify deep learning models are suggested to handle the data insufficiency problem in deep learning based on COVID-19 detection.

Keywords: COVID-19, CT dataset, chest X-ray dataset, deep learning, data augmentation, transfer learning.

[1]
Zheng C, Deng X, Fu Q, et al. Deep learning-based detection for COVID-19 from chest CT using weak label. MedRxiv 2020.
[http://dx.doi.org/10.1101/2020.03.12.20027185]
[2]
Wang B, Jin S, Yan Q, et al. AI-assisted CT imaging analysis for COVID-19 screening: Building and deploying a medical AI system. Appl Soft Comput 2021; 98: 106897.
[http://dx.doi.org/10.1016/j.asoc.2020.106897] [PMID: 33199977]
[3]
Ardakani AA, Kanafi AR, Acharya UR, Khadem N, Mohammadi A. Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks. Comput Biol Med 2020; 121: 103795.
[http://dx.doi.org/10.1016/j.compbiomed.2020.103795] [PMID: 32568676]
[4]
Narin A, Kaya C, Pamuk Z. Automatic detection of coronavirus disease (covid-19) using X-ray images and deep convolutional neural networks. arXiv 2020; 2020: 10849.
[5]
Apostolopoulos ID, Mpesiana TA. Covid-19: Automatic detection from X-ray images utilizing transfer learning with convolutional neural networks. Phys Eng Sci Med 2020; 43(2): 635-40.
[http://dx.doi.org/10.1007/s13246-020-00865-4] [PMID: 32524445]
[6]
Bhandary A, Prabhu GA, Rajinikanth V, et al. Deep-learning framework to detect lung abnormality–A study with chest X-ray and lung C.T. scan images. Pattern Recognit Lett 2020; 129: 271-8.
[http://dx.doi.org/10.1016/j.patrec.2019.11.013]
[7]
Maghdid HS, Asaad AT, Ghafoor KZ, Sadiq AS, Khan MK. Diagnosing COVID-19 pneumonia from X-ray and C.T. images using deep learning and transfer learning algorithms. arXiv 2020; 2004: 00038.
[8]
Abbas A, Abdelsamea MM, Gaber MM. Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network. Appl Intell 2021; 51: 854-64.
[http://dx.doi.org/10.1007/s10489-020-01829-7]
[9]
Keles A, Keles MB, Keles A. COV19-CNNet and COV19-ResNet: Diagnostic inference Engines for early detection of COVID-19. Cognit Comput 2021; 13: 1-11.
[http://dx.doi.org/10.1007/s12559-020-09795-5] [PMID: 33425046]
[10]
Li L, Qin L, Xu Z, et al. Artificial intelligence distinguishes COVID-19 from community acquired pneumonia on chest CT. Radiology 2020; 296: E65-71.
[http://dx.doi.org/10.1148/radiol.2020200905] [PMID: 32191588]
[11]
Narayanan BN, Hardie RC, Krishnaraja V, Karam C, Davuluru VSP. Transfer-to-transfer learning approach for computer aided detection of covid-19 in chest radiographs. AI 2020; 1: 539-57.
[12]
Wang L, Lin ZQ, Wong A. COVID-Net: A tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images. Sci Rep 2020; 10(1): 19549.
[http://dx.doi.org/10.1038/s41598-020-76550-z] [PMID: 33177550]
[13]
Alruwaili M, Shehab A, Abd El-Ghany S. COVID-19 diagnosis using an enhanced inception-ResNetV2 deep learning model in CXR images. J Healthc Eng 2021; 2021: 6658058.
[http://dx.doi.org/10.1155/2021/6658058] [PMID: 34188790]
[14]
Ozturk T, Talo M, Yildirim EA, Baloglu UB, Yildirim O, Rajendra Acharya U. Automated detection of COVID-19 cases using deep neural networks with X-ray images. Comput Biol Med 2020; 121: 103792.
[http://dx.doi.org/10.1016/j.compbiomed.2020.103792] [PMID: 32568675]
[15]
Canayaz M. MH-COVIDNet: Diagnosis of COVID-19 using deep neural networks and meta-heuristic-based feature selection on X-ray images. Biomed Signal Process Control 2021; 64: 102257.
[http://dx.doi.org/10.1016/j.bspc.2020.102257] [PMID: 33042210]
[16]
Santa Cruz BG, Bossa MN, Soelter J, Husch AD. Public Covid-19 X-ray datasets and their impact on model bias-a systematic review of a significant problem. medRxiv 2021.
[http://dx.doi.org/10.1101/2021.02.15.21251775]
[17]
Trivedi A, Ortiz A, Desbiens J, et al. Effective deep learning approaches for predicting covid-19 outcomes from chest computed tomography volumes. medRxiv 2020.
[http://dx.doi.org/10.1101/2020.10.15.20213462]
[18]
Available from: https://github.com/ieee8023/covid-chestxray-dataset.git
[19]
Available from: https://www.kaggle.com/tawsifurrahman/covid19-radiography-database
[20]
Khan S, Islam N, Jan Z, Din IU, Rodrigues JJC. A novel deep learning based framework for the detection and classification of breast cancer using transfer learning. Pattern Recognit Lett 2019; 125: 1-6.
[http://dx.doi.org/10.1016/j.patrec.2019.03.022]
[21]
Nguyen D, Kay F, Tan J, et al. Deep learning–based COVID-19 pneumonia classification using chest CT images: Model generalizability. Front Artif Intel 2021; 4.
[22]
Silva P, Luz E, Silva G, et al. COVID-19 detection in CT images with deep learning: A voting-based scheme and cross-datasets analysis. Inform Med 2020; 20: 100427.
[23]
Farooq M, Hafeez A. Covid-resnet: A deep learning framework for screening of covid19 from radiographs. arXiv 2020; 2003: 14395.
[24]
Turian J, Bergstra J, Bengio Y. Quadratic features and deep architectures for chunking. Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers. 245-8.
[http://dx.doi.org/10.3115/1620853.1620921]
[25]
Bi N, Chen X, Xu C, Zhou A. Beamer: An end-to-end deep learning framework for unifying data cleaning in DNN model training and inference. Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 4685-9.
[26]
Shyni HM, Chitra E. A comparative study of X-ray and CT images in covid-19 detection using image processing and deep learning techniques. Computer Methods and Programs in Biomedicine Update 2022; 100054.
[27]
Zhao W, Jiang W, Qiu X. Deep learning for COVID-19 detection based on CT images. Sci Rep 2021; 11(1): 14353.
[http://dx.doi.org/10.1038/s41598-021-93832-2] [PMID: 34253822]
[28]
Lemley J, Bazrafkan S, Corcoran P. Smart augmentation learning an optimal data augmentation strategy. IEEE Access 2017; 5: 5858-69.
[http://dx.doi.org/10.1109/ACCESS.2017.2696121]
[29]
Bowles C, Chen L, Guerrero R, et al. Gan augmentation: Augmenting training data using generative adversarial networks. arXiv preprint arXiv:181010863 2018.
[30]
Jackson PT, Abarghouei AA, Bonner S, Breckon TP, Obara B. Style augmentation: Data augmentation via style randomization. In: CVPR Workshops. 2019; 6: pp. 10-1.
[31]
Cubuk ED, Zoph B, Mane D, Vasudevan V, Le QV. Autoaugment: Learning augmentation policies from data. arXiv preprint arXiv:180509501 2018.

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