The week's pick
Random Articles
Reseach Article
Deep Learning-based diagnosis of COVID-19 using Chest X-Ray Images
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 185 - Number 48 |
| Year of Publication: 2023 |
| Authors: Salah Zaher |
10.5120/ijca2023923315
|
Salah Zaher . Deep Learning-based diagnosis of COVID-19 using Chest X-Ray Images. International Journal of Computer Applications. 185, 48 ( Dec 2023), 41-49. DOI=10.5120/ijca2023923315
Abstract
COVID-19 is a highly contagious disease caused by SARS-CoV-2 and has tragically claimed over 6 million lives worldwide. Early detection is crucial for containing its spread and providing appropriate treatment. In this article, we present a system to help physicians diagnose COVID-19 pneumonia by analyzing chest radiographs (CXR). The proposed system uses a dataset of 7232 CXR images, where 80% were used for training and 20% for testing. The methodology used in the proposed system consists of four steps: preprocessing the images through image processing, extracting features from the images using a pre-trained VGG16 model as a deep learning technique for feature extraction, applying two machine learning algorithms, namely Random Forests and Support Vector Machines (SVMs), and finally comparing the performance of the two algorithms. The SVM classifier achieved a classification accuracy of 97.2% at Gamma = 0.001 and C = 5, whereas Random Forests achieved an accuracy of 87%. Thus, the SVM classifier proved to be more capable in this type of classification.
References
- World Health Organization,28May2022. [Online]. Available: https://covid19.who.int/Ding, W. and Marchionini, G. 1997 A Study on Video Browsing Strategies. Technical Report. University of Maryland at College Park.
- Sharma A, Ahmad Farouk I, Lal SK. COVID-19: A Review on the Novel Coronavirus Disease Evolution, Transmission,Detection,Control and prevention viruses. 2021 Jan 29;13(2)
- Melika Lotfi,a,b Michael R. Hamblin,c,d,e and Nima Rezaeif COVID-19: Transmission, prevention, and potential therapeutic opportunities. Clin Chim Acta. 2020 Sep; 508: 254–266.
- Manas K. Akmatov, Nadine Koch, Marius Vital, Wolfgang Ahrens, Dieter Flesch-Janys, Julia Fricke, Anja Gatzemeier, Halina Greiser, Kathrin Günther, Michels, Susanne Moebus, Alexandra Nieters, Nadia 2017 May 12. doi: 10.1038/s41598-017-01212-6 PMCID:PMC5431815
- Ai Tao, Yang Zhenlu, Hou Hongyan, Zhan Chenao, Chen Chong, Lv Wenzhi, Tao Qian, Sun Ziyong, Xia Liming (2020) Correlation of chest ct and rt-pcr testing for coronavirus disease 2019 (covid-19) in china: A report of 1014 cases. Radiology 296(2):E32–E240. https ://doi.org/10.1148/radiol.2020200642
- Fang Yicheng, Zhang Huangqi, Xie Jicheng, Lin Minjie, Ying Lingjun, Pang Peipei, Ji Wenbin (2020) Sensitivity of chest ct for covid-19: Comparison to rt-pcr. Radiology 296(2):E115–E117 https://doi.org/10.1148/ radiol. 2020200432.
- Kanne Jeffrey P, Little Brent P, Chung Jonathan H, Elicker Brett M, Ketai Loren H (2020) Essentials for radiologists on covid-19: An update–radiology scientific expert panel. Radiology 296(2):E113–E114. https://doi.org/10.1148/radiol.2020200527
- C. Zheng et al., "Deep learning-based detection for COVID-19 from chest CT using weak label", 2020.
- Y. Cao et al., "Longitudinal assessment of COVID-19 using a deep learning-based quantitative CT pipeline: Illustration of two cases", Radiology Cardiothoracic Imag., vol. 2, 2020.
- L. Huang et al., "Serial quantitative chest CT assessment of COVID-19: Deep-learning approach", Radiology Cardiothoracic Imag., vol. 2, 2020.
- . R. Hertel and R. Benlamri, "COV-SNET: A deep learning model for X-ray-based COVID-19 classification,"Informatics in Medicine Unlocked, vol. 24, no. 11, pp. 620-642, 2021.
- X. Qi, L. G. Brown, D. J. Foran, J. Nosher and I. Hacihaliloglu, "Chest X-ray image phase features for improved diagnosis of COVID-19 using convolutional neural network," International Journal for Computer Assisted Radiology and Surgery, vol. 16, no. 2, pp. 197–206, 2021.
- Hasan, M.D.K.; Ahmed, S.; Abdullah, Z.M.E.; Monirujjaman Khan, M.; Anand, D.; Singh, A.; AlZain, M.; Masud, M. Deep Learning Approaches for Detecting Pneumonia in COVID-19 Patients by Analyzing Chest X-Ray Images. Math. Probl. Eng. 2021,2021, 9929274.
- Andreas C. Müller, Sarah Guido Released September 2016 Publisher(s): O'Reilly Media, Inc.ISBN: 9781449369897"Introduction to Machine Learning with Python".
- Shakti Chourasiya, Suvrat Jain " A Study Review On Supervised Machine Learning Algorithms".International Journal of Computer Science and Engineering 2019by SSRG - IJCSE Journal Volume 6 Issue 8. doi: 10.14445/23488387/IJCSE-V6I8P104.
- Zoubin Ghahramani: Advanced Lectures on Machine Learning :ML Summer Schools 2003, Canberra, Australia, February 2-14, 2003, Tübingen, Germany, August 4-16, 2003, Revised Lectures
- Brieman L, Random Forests, Machine Learning, 45,5-32, (2001) © 2001 Kluwer Academic Publishers. Manufactured in The Netherlands.
- Mariette Awad, Rahul Khanna:"Support Vector Machines for Classification"
- April 2015 DOI: 10.1007/978-1-4302-5990-9_3
- Shrestha, A.; Mahmood, A. Review of Deep Learning Algorithms and Architectures. IEEE Access 2019, 7, 53040–53065.
- Swapna, M.; Sharma, Y.K.; Prasad, B. CNN Architectures: Alex Net, Le Net, VGG, Google Net, Res Net. Int. J. Recent Technol. Eng. 2020, 8, 953–959.
- Srikanth Tammina: Transfer learning using VGG-16 with Deep Convolutional Neural Network for Classifying Images : International Journal of Scientific and Research Publications, Volume 9, Issue 10, October 2019 143 ISSN 2250-3153.
- Brian Kenji Iwana; Ryohei Kuroki; Seiichi chidaExplaining Convolutional Neural Networks using Softmax Gradient Layer-wise Relevance Propagation :IEEE Xplore: Published in: 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW) DOI: 10.1109/ICCVW.2019.00513
- COVID-19 Chest X-ray images and Lung masks Database available at: https://www.kaggle.com/datasets/tawsifurrahman/covid19-radiography-database/
Index Terms
Keywords