Comparative Analysis of MFO, GWO and GSO for Classification of Covid-19 Chest X-Ray Images

Main Article Content

Asraa M. Mohammad
https://orcid.org/0009-0004-8231-1394
Hussien Attia
Yossra H. Ali

Abstract

Medical images play a crucial role in the classification of various diseases and conditions. One of the imaging modalities is X-rays which provide valuable visual information that helps in the identification and characterization of various medical conditions. Chest radiograph (CXR) images have long been used to examine and monitor numerous lung disorders, such as tuberculosis, pneumonia, atelectasis, and hernia. COVID-19 detection can be accomplished using CXR images as well. COVID-19, a virus that causes infections in the lungs and the airways of the upper respiratory tract, was first discovered in 2019 in Wuhan Province, China, and has since been thought to cause substantial airway damage, badly impacting the lungs of affected persons. The virus was swiftly gone viral around the world and a lot of fatalities and cases growing were recorded on a daily basis. CXR can be used to monitor the effects of COVID-19 on lung tissue. This study examines a comparison analysis of k-nearest neighbors (KNN), Extreme Gradient Boosting (XGboost), and Support-Vector Machine (SVM) are some classification approaches for feature selection in this domain using The Moth-Flame Optimization algorithm (MFO), The Grey Wolf Optimizer algorithm (GWO), and The Glowworm Swarm Optimization algorithm (GSO). For this study, researchers employed a data set consisting of two sets as follows: 9,544 2D X-ray images, which were classified into two sets utilizing validated tests: 5,500 images of healthy lungs and 4,044 images of lungs with COVID-19. The second set includes 800 images, 400 of healthy lungs and 400 of lungs affected with COVID-19. Each image has been resized to 200x200 pixels. Precision, recall, and the F1-score were among the quantitative evaluation criteria used in this study.

Article Details

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1.
Comparative Analysis of MFO, GWO and GSO for Classification of Covid-19 Chest X-Ray Images. Baghdad Sci.J [Internet]. 2023 Aug. 30 [cited 2024 May 18];20(4(SI):1540. Available from: https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/9236
Section
Special Issue - Current advances in anti-infective strategies

How to Cite

1.
Comparative Analysis of MFO, GWO and GSO for Classification of Covid-19 Chest X-Ray Images. Baghdad Sci.J [Internet]. 2023 Aug. 30 [cited 2024 May 18];20(4(SI):1540. Available from: https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/9236

References

Ali RH, Abdulsalam WH. The Prediction of COVID 19 Disease Using Feature Selection Techniques. J Phys Conf Ser. 2021; 1879(2): 1-13. https://doi.org/10.1088/1742-6596/1879/2/022083

Huang C, Wang Y, Li X, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet. 2020; 395(10223): 497-506. https://doi.org/10.1016/S0140-6736(20)30183-5

Songram P, Chomphuwiset P, Kawattikul K, Jareanpon C. Classification of chest X-ray images using a hybrid deep learning method. Indones J Electr Eng Comput Sci. 2022; 25(2): 867-874. https://doi.org/10.11591/ijeecs.v25.i2.pp867-874

Narin A, Kaya C, Pamuk Z. Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks. Pattern Anal Appl. 2021; 24(3): 1207-1220. https://doi.org/10.1007/s10044-021-00984-y

Antin B, Kravitz J, Martayan E. Detecting Pneumonia in Chest X-Rays with Supervised Learning. Semant org. 2017; (46632050): 1-5. http://cs229.stanford.edu/proj2017/final-reports/5231221.pdf

Liang G, Zheng L. A transfer learning method with deep residual network for pediatric pneumonia diagnosis. Elsevier. 2020; 187: 1-9. https://doi.org/10.1016/j.cmpb.2019.06.023

Sethy PK, Behera SK, Ratha PK, Biswas P. Detection of coronavirus disease (COVID-19) based on deep features and support vector machine. Int J Math Eng Manag Sci. 2020; 5(4): 643-651. https://doi.org/10.20944/preprints202003.0300.v1

Mohammed SN, Alkinani FS, Hassan YA. Automatic computer aided diagnostic for COVID-19 based on chest X-Ray image and particle swarm intelligence. Int J Intell Eng Syst. 2020; 13(5): 63-73. https://doi.org/10.22266/ijies2020.1031.07

Too J, Mirjalili S. A Hyper Learning Binary Dragonfly Algorithm for Feature Selection: A COVID-19 Case Study. Knowl Based Syst. 2021; 212: 1-29. https://doi.org/10.1016/j.knosys.2020.106553

Bezdan T, Cvetnic D, Gajic L, Zivkovic M, Strumberger I, Bacanin N. Feature Selection by Firefly Algorithm with Improved Initialization Strategy. ACM Int Conf Proceeding Ser. 2021; 8: 1-8. https://doi.org/10.1145/3459960.3459974

Zou L, Zhou S, Li X. An Efficient Improved Greedy Harris Hawks Optimizer and Its Application to Feature Selection. Entropy. 2022; 24(8): 1-22. https://doi.org/10.3390/e24081065

Issa AS, Ali YH. Comparative Analysis of Swarm Algorithms to Classification of covid19 on X-Rays. 2022 Int Conf Data Sci Intell Comput. 2022; 22883321(Icdsic): 164-169. https://doi.org/10.1109/ICDSIC56987.2022.10075733

Sahoo SK, Saha AK, Ezugwu AE, Agushaka J O, Abuhaija B, Alsoud A R, et al. Moth Flame Optimization: Theory, Modifications, Hybridizations, and Applications. Arch Comput Methods Eng. 2023; 30(1): 391-426. https://doi.org/10.1007/s11831-022-09801-z

Mirjalili S, Mirjalili SM, Lewis A. Grey Wolf Optimizer. Adv Eng Softw. 2014; 69: 46-61. https://doi.org/10.1016/j.advengsoft.2013.12.007

Zainal N, Zain A, Radzi N, Udin A. Glowworm Swarm Optimization (GSO) Algorithm for Optimization Problems: A State-of-the-Art Review. Appl Mech Mater. 2013; 421: 507-511. https://doi.org/10.4028/www.scientific.net/AMM.421.507

Mazher AN, Waleed J. Retina based glowworm swarm optimization for random cryptographic key generation. Baghdad Sci J. 2022; 19(1): 179-188. https://doi.org/10.21123/BSJ.2022.19.1.0179

Yarinezhad R, Sarabi A. New Routing Algorithm for Vehicular Ad-hoc Networks based on Glowworm Swarm Optimization Algorithm. J artif intel data min. 2019; 7(1): 69-76. https://doi.org/10.22044/JADM.2018.6516.1765

Stimper V, Bauer S, Ernstorfer R, Schölkopf B, Xian RP. Multidimensional Contrast Limited Adaptive Histogram Equalization. IEEE Access. 2019; 7: 165437-165447. https://doi.org/10.1109/ACCESS.2019.2952899

Al Okashi OM, Ahmed IT, Abed LH. COVID-19 detection based on combined domain features. Indones J Electr Eng Comput Sci. 2022; 26(2): 965-973. https://doi.org/10.11591/ijeecs.v26.i2.pp965-973

Vigneshl T, Thyagharajan KK. Local binary pattern texture feature for satellite imagery classification. Int Conf Sci Eng Manag. 2014; 32331: 1-6. https://doi.org/10.1109/ICSEMR.2014.7043591

Battur R, Narayana J. Classification of medical X-ray images using supervised and unsupervised learning approaches. Indones J Electr Eng Comput Sci. 2023; 30(3): 1713-1721. https://doi.org/10.11591/ijeecs.v30.i3.pp1713-1721

Eds DA jumeily. Emerging Technology Trends in Internet of Things and Computing. springer; 2021. https://doi.org/10.1007/978-3-030-97255-4

Samsir S, Sitorus JHP, Ritonga Z, Aini F. Comparison of machine learning algorithms for chest X-ray image COVID-19 classification. 2021; 012040: 1-7. https://doi.org/10.1088/1742-6596/1933/1/012040

Mohamed Ali SS, Alsaeedi AH, Al-Shammary D, Alsaeedi HH, Abid HW. Efficient intelligent system for diagnosis pneumonia (SARSCOVID19) in X-ray images empowered with initial clustering. Indones J Electr Eng Comput Sci. 2021; 22(1): 241-251. https://doi.org/10.11591/ijeecs.v22.i1.pp241-251

Ali ZA, Abduljabbar ZH, Taher HA, Sallow AB, Almufti SM. Exploring the Power of eXtreme Gradient Boosting Algorithm in Machine Learning : a Review. Acad J Nawroz Univ. 2023; 12(2): 320-15. https://doi.org/10.25007/ajnu.v12n2a1612

Sahlol AT, Yousri D, Ewees AA, Al MAA. OPEN COVID ‑ 19 image classification using deep features and fractional ‑ order marine predators algorithm. Sci Rep. 2020; 10: 15364: 1-15. https://doi.org/10.1038/s41598-020-71294-2

Abdullah TH, Alizadeh F, Abdullah BH. COVID-19 Diagnosis System using SimpNet Deep Model. Baghdad Sci J. 2022; 19(5): 1078-1089. https://doi.org/10.21123/bsj.2022.6074

Kaggle. Chest X-Ray Images (Pneumonia) | Kaggle. Kaggle’s chest X-ray images (Pneumonia) dataset. Published 2020. https://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia

El-Shafai W, E. Abd El-Samie F. Extensive COVID-19 X-Ray and CT Chest Images Dataset. Mendeley Data. Published online 2020. https://data.mendeley.com/datasets/8h65ywd2jr/3

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