A robust CNN Model for Diagnosis of COVID-19 based on CT scan images and DL techniques
Abstract
The 2019 Coronavirus (COVID-19) virus has caused damage on people's respiratory systems over the world. Computed Tomography (CT) is a faster complement for RT-PCR during peak virus spread times. Nowadays, Deep Learning (DL) with CT provides more robust and reliable methods for classifying patterns in medical pictures. In this paper, we proposed a simple low training proposed customized Convolutional Neural Networks (CNN) customized model based on CNN architecture that layers which are optionals may be included such as the layer of batch normalization to reduce time taken for training and a layer with a dropout to deal with overfitting. We employed a huge dataset of chest CT slices images from diverse sources COVIDx-CT, which consists of a 16,146-image dataset with 810 patients of various nationalities. The proposed customized model's classification results compared to the VGG-16, Alex Net, and ResNet50 Deep Learning models. The proposed CNN model shows robustness by achieving an overall accuracy of 93% compared to 88%, 89%, and 95% for the VGG-16, Alex Net, and ResNet50 DL models for the classification of 3 classes. When this relates to binary classification, the classification accuracy of the proposed model and the VGG-16 models were identical (almost 100% accurate), with 0.17% of misclassification in the class of Non-Covid-19, the Alex Net model achieved almost 100% classification accuracy with 0.33% misclassification in the class of Non-Covid-19. Finally, ResNet50 achieved 95% classification accuracy with 5% misclassification in the Non-Covid-19 class.
References
Marco Ciotti, Massimo Ciccozzi, Alessandro Terrinoni, Wen-Can Jiang, Cheng-Bin Wang & Sergio Bernardini (2020) The COVID-19 pandemic, Critical Reviews in Clinical Laboratory Sciences, 57:6, 365-388, DOI: 10.1080/10408363.2020.1783198.
Skegg, D., Gluckman, P., Boulton, G., Hackmann, H., Karim, S. S., Piot, P., & Woopen, C. (2021). Future scenarios for the COVID-19 pandemic. The Lancet, 397(10276), 777–778. https://doi.org/10.1016/s0140-6736(21)00424-4.
E. Mathieu et al., “A global database of COVID-19 vaccinations,” Nat. Hum. Behav., vol. 5, no. 7, pp. 947–953, Jul. 2021, doi: 10.1038/S41562-021-01122-8.
I. Katsamenis, E. Protopapadakis, A. Voulodimos, A. Doulamis, and N. Doulamis, “Transfer Learning for COVID-19 Pneumonia Detection and Classification in Chest X-ray Images,” in 24th Pan-Hellenic Conference on Informatics, Nov. 2020, pp. 170–174, doi: 10.1145/3437120.3437300.
F. Shi et al., “Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation, and Diagnosis for COVID-19,” IEEE Reviews in Biomedical Engineering, vol. 14. pp. 4–15, 2021, doi: 10.1109/RBME.2020.2987975.
Shah, V., Keniya, R., Shridharani, A., Punjabi, M., Shah, J., & Mehendale, N. (2021). Diagnosis of COVID-19 using CT scan images and deep learning techniques. Emergency Radiology, 28(3), 497–505. https://doi.org/10.1007/s10140-020-01886-y.
[Book] Mahrishi, M., Hiran, K. K., Meena, G., & Sharma, P. (2020). Machine learning and deep learning in real-time applications. IGI Global.
[Book] Deng, L., & Yu, D. (2014). Deep learning: Methods and applications. now Publishers Inc.
[Book] Gulli, A., & Pal, S. (2017). Deep learning with Keras: Implementing deep learning models and neural networks with the power of Python. Packt Publishing.
T. D. Pham, “Classification of COVID-19 chest X-rays with deep learning: new models or fine-tuning?” Heal. Inf. Sci. Syst., vol. 9, no. 1, p. 2, Dec. 2021, DOI: 10.1007/s13755-020-00135-3.
A. Narin, C. Kaya, and Z. Pamuk, “Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks,” Pattern Anal. Appl., vol. 24, no. 3, pp. 1207–1220, Aug. 2021, DOI: 10.1007/s10044-021-00984-y.
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,” Jun. 2020, [Online]. Available: http://arxiv.org/abs/2006.11988.
Jaiswal, A.K.; Tiwari, P.;Rathi,V.K., Qian, J.; Pandey, H.M.;Albuquerque, V.H.C.: Covidpen: a novel COVID-19 detection model using chest X-rays and CT scans. medrxiv (2020).
Wang, L.; Lin, Z.Q.;Wong, A.: Covid-net: A tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images. Sci. Rep. 10(1), 1–12 (2020).
S. Asif, Y. Wenhui, H. Jin, and S. Jinhai, “Classification of COVID-19 from Chest X-ray images using Deep Convolutional Neural Network,” in 2020 IEEE 6th International Conference on Computer and Communications (ICCC), Dec. 2020, pp. 426–433, DOI: 10.1109/ICCC51575.2020.9344870.
I. D. Apostolopoulos, S. I. Aznaouridis, and M. A. Tzani, “Extracting Possibly Representative COVID-19 Biomarkers from X-ray Images with Deep Learning Approach and Image Data Related to Pulmonary Diseases,” J. Med. Biol. Eng., vol. 40, no. 3, pp. 462–469, Jun. 2020, DOI: 10.1007/s40846-020-00529-4.
“Kaggle: Your Home for Data Science.” https://www.kaggle.com/tawsifurrahman/ covid19-radiography-database (accessed Mar. 14, 2022).
Z. Li et al., “From community-acquired pneumonia to COVID-19: a deep learning–based method for quantitative analysis of COVID-19 on thick-section CT scans,” Eur. Radiol., vol. 30, no. 12, pp. 6828–6837, Dec. 2020.
Neha Rajawat, Bharat Singh Hada, Mayank Meghawat, and Soniya Lalwani, Rajesh Kumar4, “C-COVIDNet: A CNN Model for COVID-19 Detection Using Image Processing,” Arabian Journal for Science and Engineering,30 April 2022.
Alom, Md. Zahangir & Taha, Tarek & Yakopcic, Christopher & Westberg, Stefan & Hasan, Mahmudul & Esesn, Brian & Awwal, Abdul & Asari, Vijayan. (2018). The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches.
Mascarenhas and M. Agarwal, "A comparison between VGG16, VGG19 and ResNet50 architecture frameworks for Image Classification," 2021 International Conference on Disruptive Technologies for Multi-Disciplinary Research and Applications (CENTCON), 2021, pp. 96-99.
Guan, Qing & Wang, Yunjun & Ping, Bo & Li, Duanshu & Du, Jiajun & Yu, Qin & Lu, Hongtao & Wan, Xiaochun & Xiang, Jun. (2019). Deep convolutional neural network VGG-16 model for differential diagnosing of papillary thyroid carcinomas in cytological images: A pilot study Journal of Cancer.
Api documentation: Tensorflow core v2.5.0. TensorFlow. (n.d.). https://www.tensorflow.org/api_docs.
Team, K. (n.d.). Keras documentation: Keras API reference. Keras. https://keras.io/api/.
Covidx ct. Kaggle. (n.d.).
https://www.kaggle.com/hgunraj/covidxct.
Matplotlib. Overview - Matplotlib 3.4.3 documentation. (n.d.). https://matplotlib.org/stable/contents.html.
Downloads
Published
Issue
Section
License
Copyright (c) 2022 International Journal of Electronics and Telecommunications
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
1. License
The non-commercial use of the article will be governed by the Creative Commons Attribution license as currently displayed on https://creativecommons.org/licenses/by/4.0/.
2. Author’s Warranties
The author warrants that the article is original, written by stated author/s, has not been published before, contains no unlawful statements, does not infringe the rights of others, is subject to copyright that is vested exclusively in the author and free of any third party rights, and that any necessary written permissions to quote from other sources have been obtained by the author/s. The undersigned also warrants that the manuscript (or its essential substance) has not been published other than as an abstract or doctorate thesis and has not been submitted for consideration elsewhere, for print, electronic or digital publication.
3. User Rights
Under the Creative Commons Attribution license, the author(s) and users are free to share (copy, distribute and transmit the contribution) under the following conditions: 1. they must attribute the contribution in the manner specified by the author or licensor, 2. they may alter, transform, or build upon this work, 3. they may use this contribution for commercial purposes.
4. Rights of Authors
Authors retain the following rights:
- copyright, and other proprietary rights relating to the article, such as patent rights,
- the right to use the substance of the article in own future works, including lectures and books,
- the right to reproduce the article for own purposes, provided the copies are not offered for sale,
- the right to self-archive the article
- the right to supervision over the integrity of the content of the work and its fair use.
5. Co-Authorship
If the article was prepared jointly with other authors, the signatory of this form warrants that he/she has been authorized by all co-authors to sign this agreement on their behalf, and agrees to inform his/her co-authors of the terms of this agreement.
6. Termination
This agreement can be terminated by the author or the Journal Owner upon two months’ notice where the other party has materially breached this agreement and failed to remedy such breach within a month of being given the terminating party’s notice requesting such breach to be remedied. No breach or violation of this agreement will cause this agreement or any license granted in it to terminate automatically or affect the definition of the Journal Owner. The author and the Journal Owner may agree to terminate this agreement at any time. This agreement or any license granted in it cannot be terminated otherwise than in accordance with this section 6. This License shall remain in effect throughout the term of copyright in the Work and may not be revoked without the express written consent of both parties.
7. Royalties
This agreement entitles the author to no royalties or other fees. To such extent as legally permissible, the author waives his or her right to collect royalties relative to the article in respect of any use of the article by the Journal Owner or its sublicensee.
8. Miscellaneous
The Journal Owner will publish the article (or have it published) in the Journal if the article’s editorial process is successfully completed and the Journal Owner or its sublicensee has become obligated to have the article published. Where such obligation depends on the payment of a fee, it shall not be deemed to exist until such time as that fee is paid. The Journal Owner may conform the article to a style of punctuation, spelling, capitalization and usage that it deems appropriate. The Journal Owner will be allowed to sublicense the rights that are licensed to it under this agreement. This agreement will be governed by the laws of Poland.
By signing this License, Author(s) warrant(s) that they have the full power to enter into this agreement. This License shall remain in effect throughout the term of copyright in the Work and may not be revoked without the express written consent of both parties.
