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Current Medical Imaging

Editor-in-Chief

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

Research Article

A Deep Learning based Solution (Covi-DeteCT) Amidst COVID-19

Author(s): Kavita Pandey*

Volume 19, Issue 5, 2023

Published on: 19 October, 2022

Article ID: e280922209258 Pages: 16

DOI: 10.2174/1573405618666220928145344

Price: $65

Abstract

Background: The whole world has been severely affected due to the COVID-19 pandemic. The rapid and large-scale spread has caused immense pressure on the medical sector hence increasing the chances of false detection due to human errors and mishandling of reports. At the time of outbreaks of COVID-19, there is a crucial shortage of test kits as well. Quick diagnostic testing has become one of the main challenges. For the detection of COVID-19, many Artificial Intelligence based methodologies have been proposed, a few had suggested integration of the model on a public usable platform, but none had executed this on a working application as per our knowledge.

Objective: Keeping the above comprehension in mind, the objective is to provide an easy-to-use platform for COVID-19 identification. This work would be a contribution to the digitization of health facilities. This work is a fusion of deep learning classifiers and medical images to provide a speedy and accurate identification of the COVID-19 virus by analyzing the user's CT scan images of the lungs. It will assist healthcare workers in reducing their workload and decreasing the possibility of false detection.

Methods: In this work, various models like Resnet50V2 and Resnet101V2, an adjusted rendition of ResNet101V2 with Feature Pyramid Network, have been applied for classifying the CT scan images into the categories: normal or COVID-19 positive.

Results: A detailed analysis of all three models' performances have been done on the SARS-CoV-2 dataset with various metrics like precision, recall, F1-score, ROC curve, etc. It was found that Resnet50V2 achieves an accuracy of 96.79%, whereas Resnet101V2 achieves an accuracy of 97.79%. An accuracy of 98.19% has been obtained by ResNet101V2 with Feature Pyramid Network. As Res- Net101V2 with Feature Pyramid Network is showing better results, thus, it is further incorporated into a working application that takes CT images as input from the user and feeds into the trained model and detects the presence of COVID-19 infection.

Conclusion: A mobile application integrated with the deeper variant of ResNet, i.e., ResNet101V2 with FPN checks the presence of COVID-19 in a faster and accurate manner. People can use this application on their smart mobile devices. This automated system would assist healthcare workers as well, which ultimately reduces their workload and decreases the possibility of false detection.

Keywords: Pandemics, COVID-19, CT scan, deep learning, ResNet, feature pyramid network.

Graphical Abstract
[1]
MoHFW. Facilitator Guide-COVID 19 copy. 2020. Available from: https://www.mohfw.gov.in/pdf/FacilitatorGuideCOVID19_27%20March.pdf (Accessed on: October, 2020).
[2]
World Health Organization. Coronavirus (COVID-19) events as they happen. 2020. Available from: www.who.int/emergencies/diseases/novel-coronavirus-2019/events-as-they-happen (Accessed on: October, 2020).
[3]
Statista. Mobile internet users in India 2010-2040. Available from: www.statista.com/statistics/558610/number-of-mobile-internet-user-in-india/ (Accessed on: September, 2020).
[4]
Angelov P, Almeida SE. SARS-CoV-2 CT-scan dataset: A large dataset of real patients CT scans for SARS-CoV-2 identification. medRxiv 20078584.2020;
[5]
Ozturk T, Talo M, Yildirim EA, Baloglu UB, Yildirim O, Rajendra AU. 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]
[6]
Mahmud T, Rahman MA, Fattah SA. CovXNet: A multi-dilation convolutional neural network for automatic COVID-19 and other pneumo-nia detection from chest X-ray images with transferable multi-receptive feature optimization. Comput Biol Med 2020; 122: 103869.
[http://dx.doi.org/10.1016/j.compbiomed.2020.103869] [PMID: 32658740]
[7]
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]
[8]
Li L, Qin L, Xu Z, et al. Using artificial intelligence to detect COVID-19 and community-acquired pneumonia based on pulmonary CT: Evaluation of the diagnostic accuracy. Radiology 2020; 296(2): E65-71.
[http://dx.doi.org/10.1148/radiol.2020200905] [PMID: 32191588]
[9]
Gozes O, Frid-Adar M, Greenspan H, Browning PD, Zhang H, Ji W, et al. Rapid ai development cycle for the coronavirus (covid-19) pandemic: Initial results for automated detection & patient monitoring using deep learning ct image analysis. arXiv preprint arXiv 2020. arXiv:2003.05037.
[10]
Adhikari ND. Infection severity detection of CoVID19 from X-Rays and CT scans using artificial intelligence. Int J Comput 2020; 38(1): 73-92.
[11]
Toğaçar M, Ergen B, Cömert Z. 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; 121: 103805.
[http://dx.doi.org/10.1016/j.compbiomed.2020.103805] [PMID: 32568679]
[12]
Shibly KH, Dey SK, Islam MTU, Rahman MM. COVID faster R–CNN: A novel framework to diagnose novel coronavirus disease (COVID-19) in X-Ray images. Inform Med Unlocked 2020; 20: 100405.
[http://dx.doi.org/10.1016/j.imu.2020.100405] [PMID: 32835082]
[13]
Rahimzadeh M, Attar A, Sakhaei SM. A fully automated deep learning-based network for detecting COVID-19 from a new and large lung CT scan dataset. Biomed Signal Process Control 2021; 68: 102588.
[http://dx.doi.org/10.1016/j.bspc.2021.102588] [PMID: 33821166]
[14]
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 Unlocked 2020; 20: 100427.
[15]
Ko H, Chung H, Kang WS, et al. COVID-19 pneumonia diagnosis using a simple 2D deep learning framework with a single chest CT image: Model development and validation. J Med Internet Res 2020; 22(6): e19569.
[http://dx.doi.org/10.2196/19569] [PMID: 32568730]
[16]
Mei X, Lee HC, Diao K, et al. Artificial intelligence–enabled rapid diagnosis of patients with COVID-19. Nat Med 2020; 26(8): 1224-8.
[http://dx.doi.org/10.1038/s41591-020-0931-3] [PMID: 32427924]
[17]
Panwar H, Gupta PK, Siddiqui MK, Morales-Menendez R, Bhardwaj P, Singh V. A deep learning and grad-CAM based color visualization approach for fast detection of COVID-19 cases using chest X-ray and CT-Scan images. Chaos Solitons Fractals 2020; 140: 110190.
[http://dx.doi.org/10.1016/j.chaos.2020.110190] [PMID: 32836918]
[18]
Iwendi C, Bashir AK, Peshkar A, et al. COVID-19 patient health prediction using boosted random forest algorithm. Front Public Health 2020; 8: 357.
[http://dx.doi.org/10.3389/fpubh.2020.00357] [PMID: 32719767]
[19]
Khanday AMUD, Rabani ST, Khan QR, Rouf N, Mohi Ud Din M. Machine learning based approaches for detecting COVID-19 using clini-cal text data. Int J Inform Technol 2020; 12(3): 731-9.
[http://dx.doi.org/10.1007/s41870-020-00495-9] [PMID: 32838125]
[20]
He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition. 2016; pp. 770-8.
[21]
Wu H, Xin M, Fang W, Hu HM, Hu Z. Multi-level feature network with multi-loss for person re-identification. IEEE Access 2019; 7: 91052-62.
[http://dx.doi.org/10.1109/ACCESS.2019.2927052]
[22]
Lin TY, Dollár P, Girshick R, He K, Hariharan B, Belongie S. Feature pyramid networks for object detection. Proceedings of the IEEE conference on computer vision and pattern recognition. 2017; pp. 2117-25.
[23]
Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 2012; 25.
[24]
Van Rossum G. The Python Library Reference, release 3.8. 2. Python Software Foundation 2020; 36 Available from: https://www.python.org/downloads/release/python-382/

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