A Fast and Reliable Approach for COVID-19 Detection from CT-Scan Images
Downloads
Background: COVID-19 is a highly contagious respiratory disease with multiple mutant variants, an asymptotic nature in patients, and with potential to stay undetected in common tests, which makes it deadlier, more transmissible, and harder to detect. Regardless of variants, the COVID-19 infection shows several observable anomalies in the computed tomography (CT) scans of the lungs, even in the early stages of infection. A quick and reliable way of detecting COVID-19 is essential to manage the growing transmission of COVID-19 and save lives.
Objective: This study focuses on developing a deep learning model that can be used as an auxiliary decision system to detect COVID-19 from chest CT-scan images quickly and effectively.
Methods: In this research, we propose a MobileNet-based transfer learning model to detect COVID-19 in CT-scan images. To test the performance of our proposed model, we collect three publicly available COVID-19 CT-scan datasets and prepare another dataset by combining the collected datasets. We also implement a mobile application using the model trained on the combined dataset, which can be used as an auxiliary decision system for COVID-19 screening in real life.
Results: Our proposed model achieves a promising accuracy of 96.14% on the combined dataset and accuracy of 98.75%, 98.54%, and 97.84% respectively in detecting COVID-19 samples on the collected datasets. It also outperforms other transfer learning models while having lower memory consumption, ensuring the best performance in both normal and low-powered, resource-constrained devices.
Conclusion: We believe, the promising performance of our proposed method will facilitate its use as an auxiliary decision system to detect COVID-19 patients quickly and reliably. This will allow authorities to take immediate measures to limit COVID-19 transmission to prevent further casualties as well as accelerate the screening for COVID-19 while reducing the workload of medical personnel.
Keywords: Auxiliary Decision System, COVID-19, CT Scan, Deep Learning, MobileNet, Transfer Learning
World Health Organization (WHO), "WHO Coronavirus (COVID-19) Dashboard.” https://covid19.who.int (accessed Jul. 07, 2023).
"Clinical characteristics of COVID-19,” European Centre for Disease Prevention and Control. https://www.ecdc.europa.eu/en/covid-19/latest-evidence/clinical (accessed Jan. 14, 2022).
Q. Ma et al., "Global Percentage of Asymptomatic SARS-CoV-2 Infections Among the Tested Population and Individuals With Confirmed COVID-19 Diagnosis: A Systematic Review and Meta-analysis,” JAMA Netw. Open, vol. 4, no. 12, p. e2137257, Dec. 2021, doi: 10.1001/jamanetworkopen.2021.37257.
B. Nogrady, "What the data say about asymptomatic COVID infections,” Nature, vol. 587, no. 7835, pp. 534–535, Nov. 2020, doi: 10.1038/d41586-020-03141-3.
D. P. Oran and E. J. Topol, "The Proportion of SARS-CoV-2 Infections That Are Asymptomatic: A Systematic Review,” Ann. Intern. Med., vol. 174, no. 5, pp. 655–662, May 2021, doi: 10.7326/M20-6976.
"SARS-CoV-2 Viral Mutations: Impact on COVID-19 Tests,” FDA, Dec. 2021, Accessed: Mar. 26, 2023. [Online]. Available: https://www.fda.gov/medical-devices/coronavirus-covid-19-and-medical-devices/sars-cov-2-viral-mutations-impact-covid-19-tests
"Tracking SARS-CoV-2 variants.” https://www.who.int/activities/tracking-SARS-CoV-2-variants (accessed Jan. 11, 2023).
"SARS-CoV-2 Variant Classifications and Definitions,” Centers for Disease Control and Prevention, Feb. 11, 2023. https://www.cdc.gov/coronavirus/2019-ncov/variants/variant-classifications.html (accessed Jan. 01, 2023).
E. Callaway, "The mutation that helps Delta spread like wildfire,” Nature, vol. 596, no. 7873, pp. 472–473, Aug. 2021, doi: 10.1038/d41586-021-02275-2.
A. Fowlkes, "Effectiveness of COVID-19 Vaccines in Preventing SARS-CoV-2 Infection Among Frontline Workers Before and During B.1.617.2 (Delta) Variant Predominance ” Eight U.S. Locations, December 2020–August 2021,” MMWR Morb. Mortal. Wkly. Rep., vol. 70, 2021, doi: 10.15585/mmwr.mm7034e4.
J. Lopez Bernal et al., "Effectiveness of Covid-19 Vaccines against the B.1.617.2 (Delta) Variant,” N. Engl. J. Med., vol. 385, no. 7, pp. 585–594, Aug. 2021, doi: 10.1056/NEJMoa2108891.
C. M. Brown, "Outbreak of SARS-CoV-2 Infections, Including COVID-19 Vaccine Breakthrough Infections, Associated with Large Public Gatherings ” Barnstable County, Massachusetts, July 2021,” MMWR Morb. Mortal. Wkly. Rep., vol. 70, 2021, doi: 10.15585/mmwr.mm7031e2.
V. Papanikolaou et al., "From delta to Omicron: S1-RBD/S2 mutation/deletion equilibrium in SARS-CoV-2 defined variants,” Gene, vol. 814, p. 146134, Mar. 2022, doi: 10.1016/j.gene.2021.146134.
J. A. Lewnard, V. X. Hong, M. M. Patel, R. Kahn, M. Lipsitch, and S. Y. Tartof, "Clinical outcomes associated with SARS-CoV-2 Omicron (B.1.1.529) variant and BA.1/BA.1.1 or BA.2 subvariant infection in Southern California,” Nat. Med., vol. 28, no. 9, Art. no. 9, Sep. 2022, doi: 10.1038/s41591-022-01887-z.
T. Ai et al., "Correlation of Chest CT and RT-PCR Testing for Coronavirus Disease 2019 (COVID-19) in China: A Report of 1014 Cases,” Radiology, vol. 296, no. 2, pp. 32–40, 2020, doi: 10.1148/radiol.2020200642.
A. Dangis et al., "Accuracy and Reproducibility of Low-Dose Submillisievert Chest CT for the Diagnosis of COVID-19,” Radiol. Cardiothorac. Imaging, vol. 2, no. 2, 2020, doi: 10.1148/ryct.2020200196.
A. Borakati, A. Perera, J. Johnson, and T. Sood, "Diagnostic Accuracy of X-Ray versus CT in COVID-19: A Propensity-Matched Database Study,” BMJ Open, vol. 10, no. 11, Nov. 2020, doi: 10.1136/bmjopen-2020-042946.
Md. M. Islam, F. Karray, R. Alhajj, and J. Zeng, "A Review on Deep Learning Techniques for the Diagnosis of Novel Coronavirus (COVID-19),” IEEE Access, vol. 9, pp. 30551–30572, 2021, doi: 10.1109/ACCESS.2021.3058537.
H. Jindal et al., "False-Negative RT-PCR Findings and Double Mutant Variant as Factors of an Overwhelming Second Wave of COVID-19 in India: an Emerging Global Health Disaster,” SN Compr. Clin. Med., vol. 3, no. 12, pp. 2383–2388, Dec. 2021, doi: 10.1007/s42399-021-01059-z.
P. Fillatre et al., "A new SARS-CoV-2 variant poorly detected by RT-PCR on nasopharyngeal samples, with high lethality,” Infectious Diseases (except HIV/AIDS), preprint, May 2021. doi: 10.1101/2021.05.05.21256690;
Y. H. Jin et al., "A Rapid Advice Guideline for the Diagnosis and Treatment of 2019 Novel Coronavirus (2019-nCoV) Infected Pneumonia (Standard Version),” Mil. Med. Res., vol. 7, no. 1, p. 4, Feb. 2020, doi: 10.1186/s40779-020-0233-6.
F. M. Yener and A. B. Oktay, "Diagnosis of COVID-19 with a Deep Learning Approach on Chest CT Slices,” in 2020 Medical Technologies Congress (TIPTEKNO), Antalya: IEEE, Nov. 2020, pp. 1–4. doi: 10.1109/TIPTEKNO50054.2020.9299266.
K. Simonyan and A. Zisserman, "Very Deep Convolutional Networks for Large-Scale Image Recognition,” arXiv:1409.1556, Apr. 2015, Accessed: Dec. 12, 2021. [Online]. Available: http://arxiv.org/abs/1409.1556
F. Chollet, "Xception: Deep Learning With Depthwise Separable Convolutions,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jul. 2017, pp. 1800–1807. doi: 10.1109/CVPR.2017.195.
X. Yang, X. He, J. Zhao, Y. Zhang, S. Zhang, and P. Xie, "COVID-CT-Dataset: A CT Scan Dataset about COVID-19,” arXiv:2003.13865, Jun. 2020, Accessed: Dec. 12, 2021. [Online]. Available: http://arxiv.org/abs/2003.13865
"Kaggle: Your Machine Learning and Data Science Community.” https://www.kaggle.com/ (accessed Jan. 09, 2023).
"ImageNet.” https://image-net.org/index.php (accessed Dec. 18, 2021).
X. Wu, Z. Wang, and S. Hu, "Recognizing COVID-19 Positive: Through CT Images,” in 2020 Chinese Automation Congress (CAC), Shanghai, China: IEEE, Nov. 2020, pp. 4572–4577. doi: 10.1109/CAC51589.2020.9326470.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, "ImageNet Classification with Deep Convolutional Neural Networks,” in Advances in Neural Information Processing Systems, Curran Associates, Inc., 2012, pp. 1097–1105. [Online]. Available: https://proceedings.neurips.cc/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf
K. He, X. Zhang, S. Ren, and J. Sun, "Deep Residual Learning for Image Recognition,” arXiv:1512.03385, Dec. 2015, Accessed: Dec. 12, 2021. [Online]. Available: http://arxiv.org/abs/1512.03385
F. N. Iandola, S. Han, M. W. Moskewicz, K. Ashraf, W. J. Dally, and K. Keutzer, "SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size,” arXiv:1602.07360, Nov. 2016, Accessed: Dec. 12, 2021. [Online]. Available: http://arxiv.org/abs/1602.07360
G. Huang, Z. Liu, L. van der Maaten, and K. Q. Weinberger, "Densely Connected Convolutional Networks,” arXiv:1608.06993, Jan. 2018, Accessed: Dec. 12, 2021. [Online]. Available: http://arxiv.org/abs/1608.06993
E. Soares, P. Angelov, S. Biaso, M. H. Froes, and D. K. Abe, "SARS-CoV-2 CT-Scan Dataset: A Large Dataset of Real Patients CT Scans for SARS-CoV-2 Identification,” medRxiv, 2020, doi: 10.1101/2020.04.24.20078584.
"MNIST handwritten digit database, Yann LeCun, Corinna Cortes and Chris Burges.” http://yann.lecun.com/exdb/mnist/ (accessed Jan. 09, 2023).
T. Anwar and S. Zakir, "Deep Learning Based Diagnosis of COVID-19 using Chest CT-Scan Images,” in 2020 IEEE 23rd International Multitopic Conference (INMIC), Bahawalpur, Pakistan: IEEE, Nov. 2020, pp. 1–5. doi: 10.1109/INMIC50486.2020.9318212.
M. Tan and Q. V. Le, "EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks,” arXiv:1905.11946, Sep. 2020, Accessed: Dec. 12, 2021. [Online]. Available: http://arxiv.org/abs/1905.11946
S. Wang et al., "A Deep Learning Algorithm Using CT Images to Screen for Corona Virus Disease (COVID-19),” Eur. Radiol., vol. 31, no. 8, pp. 6096–6104, Aug. 2021, doi: 10.1007/s00330-021-07715-1.
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, "Rethinking the Inception Architecture for Computer Vision,” arXiv:1512.00567, Dec. 2015, Accessed: Dec. 12, 2021. [Online]. Available: http://arxiv.org/abs/1512.00567
Md. R. Islam and A. Matin, "Detection of COVID 19 from CT Image by the Novel LeNet-5 CNN Architecture,” in 2020 23rd International Conference on Computer and Information Technology (ICCIT), Dhaka, Bangladesh: IEEE, Dec. 2020, pp. 1–5. doi: 10.1109/ICCIT51783.2020.9392723.
X. He et al., "Sample-Efficient Deep Learning for COVID-19 Diagnosis Based on CT Scans,” medRxiv, 2020, doi: 10.1101/2020.04.13.20063941.
Md. Foysal and A. B. M. Aowlad Hossain, "COVID-19 Detection from Chest CT Images using Ensemble Deep Convolutional Neural Network,” in 2021 2nd International Conference for Emerging Technology (INCET), Belagavi, India: IEEE, May 2021, pp. 1–6. doi: 10.1109/INCET51464.2021.9456387.
M. Maftouni, A. C. C. Law, B. Shen, Y. Zhou, N. Ayoobi Yazdi, and Z. Kong, "A Robust Ensemble-Deep Learning Model for COVID-19 Diagnosis based on an Integrated CT Scan Images Database,” in Proceedings of the 2021 IISE Annual Conference and Expo, Virtual Conference: Institute of Industrial and Systems Engineers (IISE), May 2021, pp. 632–637. [Online]. Available: https://www.proceedings.com/61116.html
"medRxiv.org - the preprint server for Health Sciences.” https://www.medrxiv.org/ (accessed Apr. 05, 2023).
"bioRxiv.org - the preprint server for Biology.” https://www.biorxiv.org/ (accessed Apr. 05, 2023).
J. P. Cohen, P. Morrison, L. Dao, K. Roth, T. Q. Duong, and M. Ghassemi, "COVID-19 Image Data Collection: Prospective Predictions Are the Future,” arXiv:2006.11988, Dec. 2020, Accessed: Dec. 12, 2021. [Online]. Available: http://arxiv.org/abs/2006.11988
P. Afshar et al., "COVID-CT-MD: COVID-19 Computed Tomography (CT) Scan Dataset Applicable in Machine Learning and Deep Learning,” Sep. 2020, [Online]. Available: http://arxiv.org/abs/2009.14623
S. P. Morozov et al., "MosMedData: Chest CT Scans With COVID-19 Related Findings Dataset,” May 2020, [Online]. Available: http://arxiv.org/abs/2005.06465
M. Rahimzadeh, A. Attar, and S. M. Sakhaei, "A Fully Automated Deep Learning-Based Network for Detecting COVID-19 from a New and Large Lung CT Scan Dataset,” Biomed. Signal Process. Control, vol. 68, p. 102588, Jul. 2021, doi: 10.1016/j.bspc.2021.102588.
M. Jun et al., "COVID-19 CT Lung and Infection Segmentation Dataset.” Zenodo, Apr. 2020. doi: 10.5281/ZENODO.3757476.
"COVID-19 CT segmentation dataset,” Medical Segmentation. https://medicalsegmentation.com/covid19/ (accessed Dec. 12, 2021).
A. G. Howard et al., "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications,” arXiv:1704.04861, Apr. 2017, Accessed: Dec. 12, 2021. [Online]. Available: http://arxiv.org/abs/1704.04861
"TensorFlow,” TensorFlow. https://www.tensorflow.org/ (accessed Dec. 12, 2021).
"Keras: the Python deep learning API.” https://keras.io/ (accessed Apr. 06, 2023).
"Google Colab.” https://colab.research.google.com/ (accessed Dec. 12, 2021).
D. P. Kingma and J. Ba, "Adam: A Method for Stochastic Optimization,” arXiv:1412.6980, Jan. 2017, Accessed: Dec. 12, 2021. [Online]. Available: http://arxiv.org/abs/1412.6980
H. Panwar, P. K. Gupta, M. K. Siddiqui, R. Morales-Menendez, P. Bhardwaj, and V. Singh, "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, vol. 140, p. 110190, Nov. 2020, doi: 10.1016/j.chaos.2020.110190.
M. Dhruv, R. Sai Chandra Teja, R. Sri Devi, and S. Nagesh Kumar, "InRFNet: Involution Receptive Field Network for COVID-19 Diagnosis,” J. Phys. Conf. Ser., vol. 2161, no. 1, p. 012064, Jan. 2022, doi: 10.1088/1742-6596/2161/1/012064.
A. Ouahab, "Multimodal Convolutional Neural Networks for Detection of Covid-19 Using Chest X-Ray and CT Images,” Opt. Mem. Neural Netw., vol. 30, no. 4, pp. 276–283, Oct. 2021, doi: 10.3103/S1060992X21040044.
A. P. Hartono, C. R. Luhur, C. A. Indriyani, C. R. Wijaya, N. N. Qomariyah, and A. A. Purwita, "Evaluating Deep Learning for CT Scan COVID-19 Automatic Detection,” in 2021 International Conference on ICT for Smart Society (ICISS), Aug. 2021, pp. 1–7. doi: 10.1109/ICISS53185.2021.9533224.
Copyright (c) 2023 The Authors. Published by Universitas Airlangga.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
All accepted papers will be published under a Creative Commons Attribution 4.0 International (CC BY 4.0) License. Authors retain copyright and grant the journal right of first publication. CC-BY Licenced means lets others to Share (copy and redistribute the material in any medium or format) and Adapt (remix, transform, and build upon the material for any purpose, even commercially).
