A New Model Design for Combating COVID -19 Pandemic Based on SVM and CNN Approaches
DOI:
https://doi.org/10.21123/bsj.2023.7403Keywords:
CNN, COVID-19, Machine Learning, Medical Imaging, RNN, SVMAbstract
In the current worldwide health crisis produced by coronavirus disease (COVID-19), researchers and medical specialists began looking for new ways to tackle the epidemic. According to recent studies, Machine Learning (ML) has been effectively deployed in the health sector. Medical imaging sources (radiography and computed tomography) have aided in the development of artificial intelligence(AI) strategies to tackle the coronavirus outbreak. As a result, a classical machine learning approach for coronavirus detection from Computerized Tomography (CT) images was developed. In this study, the convolutional neural network (CNN) model for feature extraction and support vector machine (SVM) for the classification of axial lung CT-scans into two groups (COVID-19 and NonCOVID-19) had been proposed. A dataset used is 960 slices of CT scan collected from Iraqi patients /Ibn Al-Nafis teaching hospital. The performance metrics are used in this study (accuracy, recall, precision, and F1 scores). The results indicate that the proposed approach generated a high-quality model for the collected dataset, with an overall accuracy of 98.95% and an overall recall of 97 %.
Received 9/5/2022, Accepted 7/9/2022, Published Online First 20/1/2023
References
Perumal V, Narayanan V, Rajasekar SJS. Prediction of COVID-19 with Computed Tomography Images using Hybrid Learning Techniques. Dis Markers 2021; 2021: 1–15.
Mosa ZM, Ghae NH, Ali AH. Detecting keratoconus by using SVM and decision tree classifiers with the aid of image processing. Baghdad Sci J 2019; 16(4): 1022–1029. https://doi.org/10.21123/bsj.2019.16.4(Suppl.).1022
Dogan O, Tiwari S, Jabbar MA, Guggari S. A systematic review on AI/ML approaches against COVID-19 outbreak. Complex Intell Syst 2021; 7: 2655–2678.
Tahamtan A, Ardebili A. Real-time RT-PCR in COVID-19 detection: issues affecting the results. Expert Rev Mol Diagn 2020; 20: 453–454.
Khan M, Mehran MT, Haq ZU, Ullah Z, Naqv SR, Ihsan M, et al. Applications of artificial intelligence in COVID-19 pandemic: A comprehensive review. Expert Syst Appl 2021; 185: 115695.
Sarker IH. Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions. SN Computer Science; 2. Epub ahead of print 2021. https://dx.doi.org/10.1007/s42979-021-00815-1 .
Enireddy V, Kumar MJK, Donepudi B, Karthikeyan C. Detection of COVID-19 using Hybrid ResNet and SVM. IOP Conf Ser Mater Sci Eng. 2020; 993: 012046. https://dx.doi.org/10.1088/1757-899X/993/1/012046 .
SethyPK, 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.
Turkoglu M. COVIDetectioNet: COVID-19 diagnosis system based on X-ray images using features selected from pre-learned deep features ensemble. Appl Intell 2021; 51: 1213–1226.
Sharifrazi D, Alizadehsani R, Roshanzamir M, Joloudari JH, Shoeibi A, Jafari M, et al. Fusion of convolution neural network, support vector machine and Sobel filter for accurate detection of COVID-19 patients using X-ray images. Biomed Signal Process Control 2021; 68: 102622.
Zhou C, Song J, Zhou S, Zhang Z, Xing J. COVID-19 Detection based on Image Regrouping and ResNet-SVM using Chest X-ray Images. IEEE Access. 2021; 9: 81902-81912. https://dx.doi.org/10.1109/ACCESS.2021.3086229 .
Hu R, Gan J, Zhu X, Liu T, Shi X. Multi-task multi-modality SVM for early COVID-19 Diagnosis using chest CT data. Inf Process Manag. 2022; 59(1):102782. https://dx.doi.org/10.1016/j.ipm.2021.102782 .
Rymarczyk T, Kozłowski E, Kłosowski G, Niderla K. Logistic regression for machine learning in process tomography. Sensors (Switzerland) 2019; 19: 1–19.
Majumder AB, Gupta S, Singh D, Majumder S. An intelligent system for prediction of COVID-19 case using machine learning framework-logistic regression. J Phys Conf Ser. 2021; 1797. https://dx.doi.org/10.1088/1742-6596/1797/1/012011.
Khanday AMUD, Rabani ST, Khan QR, Rouf N, Din MMU. Machine learning based approaches for detecting COVID-19 using clinical text data. Int J Inf Technol 2020; 12: 731–739.
Almeshal AM, Almazrouee AI, Alenizi MR, Alhajeri SN. Forecasting the Spread of COVID-19 in Kuwait Using Compartmental and Logistic Regression Models. Appl Sci 2020; 10: 3402.
Shaban WM, Rabie AH, Saleh AI, Abo-Elsoud MA. A new COVID-19 Patients Detection Strategy (CPDS) based on hybrid feature selection and enhanced KNN classifier. Knowl.-Based Syst. 2020; 205: 106270.
Mukherjee R, Kundu A, Mukherjee I, Gupta D, Tiwari P, Khanna A, et al. IoT-cloud based healthcare model for COVID-19 detection: an enhanced k-Nearest Neighbour classifier based approach. Computing. 2021:.1-21 https://dx.doi.org/10.1007/s00607-021-00951-9 .
Oyewola DO, Dada EG, Misra S, Damaševicius R. Predicting covid-19 cases in south korea with all k-edited nearest neighbors noise filter and machine learning techniques. Info.2021; 12(12):528. DOI: https://dx.doi.org/10.3390/info12120528 .
Hasoon JN, Fadel AH, Hameed RS, Mostafa SA, Khalaf BA, Mohammed MA. COVID-19 anomaly detection and classification method based on supervised machine learning of chest X-ray images. Results Phys 2021; 31: 0–7.
Milanović M, Stamenković M. CHAID Decision Tree: Methodological Frame and Application. Econ Themes 2016; 54: 563–586.
Yu H, Shao J ,Guo Y, Xiang Y, Sun C, Yuan Y. Data-driven discovery of a clinical route for severity detection of COVID-19 paediatric cases. IET Cyber-systems Robot 2020; 2: 205–206.
Yoo SH, Geng H, Chiu TL,Yu SK, Cho DC,Choi MS, et al. Deep Learning-Based Decision-Tree Classifier for COVID-19 Diagnosis From Chest X-ray Imaging. Front Med 2020; 7: 1–8.
Pourhomayoun M, Shakibi M. Predicting mortality risk in patients with COVID-19 using machine learning to help medical decision-making. Smart Health. 2021; 20: 1-8. https://dx.doi.org/10.1016/j.smhl.2020.100178 .
Ma J, Yuan Y. Dimension reduction of image deep feature using PCA. J Vis Commun Image Represent 2019; 63: 102578.
Aswathy A.L., S. A.H., S.S. V.C. COVID-19 diagnosis and severity detection from CT-images using transfer learning and back propagation neural network. J Infect Public Health 2021; 14(10): 1435–1445.
Wang B, Jiang L. Principal Component Analysis Applications in COVID-19 Genome Sequence Studies. Cogn Comput. 2021; 13: 1-12. https://doi.org/10.1007/s12559-020-09790-w .
Fujisawa K, Shimo M, Taguchi YH, Ikematsu S, Miyata R. PCA-based unsupervised feature extraction for gene expression analysis of COVID-19 patients. Sci Rep 2021; 11: 1–11.
Öztürk Ş, Özkaya U, Barstuğan M. Classification of Coronavirus (COVID-19) from X-ray and CT images using shrunken features. Int J Imaging Syst Technol 2021; 31: 5–15.
Wu J, Sha S. Pattern recognition of the covid-19 pandemic in the united states: Implications for disease mitigation. Int J Environ Res Public Health 2021; 18: 1–13.
Ali AM, Ghafoor KZ, Mulahuwaish A, Maghdid HS, Hussein SM, Mohammed MA. COVID-19 Pneumonia Level Detection using Deep Learning Algorithm. Techrxiv 2020; 0–16.
Arslan H, Arslan H. A new COVID-19 detection method from human genome sequences using CpG island features and KNN classifier. Eng Sci Technol an Int J 2021; 24: 839–847.
Feng C, Wang L, Chen X, Zhai Y , Zhu F, Chen H,et al. A Novel Triage Tool of Artificial Intelligence-Assisted Diagnosis Aid System for Suspected COVID-19 Pneumonia in Fever Clinics.medRxiv.2021. https://doi.org/10.1101/2020.03.19.20039099 .
Yan L, Zhang HT, Goncalves J, Xiao Y, Wang M, GuoY,et al. A machine learning-based model for survival prediction in patients with severe COVID-19 infection. medRxiv. 2020. DOI: https://dx.doi.org/10.1101/2020.02.27.20028027 .
Carrillo-Larco RM, Castillo-Cara M. Using country-level variables to classify countries according to the number of confirmed COVID-19 cases: An unsupervised machine learning approach. Wellcome Open Res 2020; 5: 1–22.
Virgantari F, Faridhan YE. K-Means Clustering of COVID-19 Cases in Indonesia’s Provinces. Int J Eng Nat Sci. 2020; 5(2): 1–7.
Liang X, Feng Z, Xue Y, et al. A 3M K-means algorithm for fast and practicably identifying COVID-19 close contacts. MATEC Web Conf 2021; 336: 06009.
Rahimi HM, Nadimi M, Langeroudi AG, Taheri M, Fard. Application of Machine Learning in Diagnosis of COVID-19 Through X-Ray and CT Images: A Scop SGing Review. Front Cardiovasc Med.2021;8: 638011 . https://dx.doi.org/10.3389/fcvm.2021.638011 .
Sadoon TAU-M, Ali MH. Coronavirus 2019 (COVID-19) Detection Based on Deep Learning. Al-Nahrain J Eng Sci 2020; 23: 408–415.
Abdulkareem JF, Aljobouri HK, Hasan AM. Chest CT Images Analysis with Deep- Learning and Handcrafted Based Algorithms for COVID-19 Diagnosis. Des Eng 2021; 6246–6262.
Hasan AM, Ibrahim RW, Jalab HA. Breast Cancer MRI Classification Based on Fractional Entropy Image Enhancement and Deep Feature Extraction. Baghdad Sci J. 2022; 20(1):221–234. https://doi.org/10.21123/bsj.2022.6782
Lin G, Shen w. Research on convolutional neural network based on improved Relu piecewise activation function. Procedia Comput. Sci.2018; 131: 977–984
Sitaula C, Aryal S. New bag of deep visual words based features to classify chest x-ray images for COVID-19 diagnosis. Heal Inf Sci Syst 2021; 9: 1–12.
Maia M, Pimentel JS, Pereira IS, Gondim J, Barreto ME, Ara A. Convolutional support vector models: Prediction of coronavirus disease using chest x-rays. Inf 2020; 11: 1–19.
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