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Automatic diagnosis of CoV-19 in CXR images using haar-like feature and XgBoost classifier

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Abstract

Many researchers and medical practitioners have recently focused on the automatic detection of COVID-19 using chest X-Ray (CXR) images. In the past, several computer-aided diagnostic (CAD) schemes were developed to assist healthcare professionals. These CAD systems using CXR images can speed up the identification process. As a result, we have proposed an automatic diagnosis (CAD-CXR) system by using the haar-like feature and XgBoost classifier. To develop a CAD-CXR system, we have used a pre-processing step and Haar-Like feature descriptor to extract features from chest X-ray images. Afterward, many machine learning classifiers are employed to categorize the images into coronavirus-19 and normal. Feature extraction and classification techniques are evaluated based on detection performance. Several experiments are performed to compare the performance of the proposed method with other classifiers. Our work is evaluated by public datasets with three different train-test splits in terms of average accuracy, F1-score, recall, and precision metrics. Compared to state-of-the-art methods, the XgBoost classifier with the Haar-Like feature obtained excellent performance based on the accuracy of 97.5%, F1-score of 96%, recall of 96%, and precision of 97%. These experimental results conclude that the CAD-CXR system outperforms to detect most of the covid-19 cases. The proposed CAD-CXR approach can be utilized to effectively screen the COVID-19-infected patients using CXR images.

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Data availability

The authors have used following public dataset for the experimental study: -Tawsif ur rehman, COVID-19 Radiography Database | Kaggle, (n.d.). https://www.kaggle.com/tawsifurrahman/covid19-radiography-database (accessed December 22, 2021.

References

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

    Article  Google Scholar 

  2. Lu H, Stratton CW, Tang YW (2020) Outbreak of pneumonia of unknown etiology in Wuhan, China: The mystery and the miracle. J Med Virol 92:401–402. https://doi.org/10.1002/jmv.25678

    Article  Google Scholar 

  3. WHO, Weekly epidemiological update on COVID-19 - 28 December 2021, (n.d.). https://www.who.int/publications/m/item/weekly-epidemiological-update-on-covid-19---28-december-2021 . Accessed 31 Dec 2021 

  4. Ozturk T, Talo M, Yildirim EA, Baloglu UB, Yildirim O, Rajendra Acharya U (2020) Automated detection of COVID-19 cases using deep neural networks with X-ray images, Comput Biol Med 121, 103792. https://doi.org/10.1016/j.compbiomed.2020.103792

  5. WHO, Classification of Omicron (B.1.1.529): SARS-CoV-2 Variant of Concern, (n.d.). https://www.who.int/news/item/26-11-2021-classification-of-omicron-(b.1.1.529)-sars-cov-2-variant-of-concern . Accessed 31 Dec 2021

  6. Chen N, Zhou M, Dong X, Qu J, Gong F, Han Y, Qiu Y, Wang J, Liu Y, Wei Y, Xia J, Yu T, Zhang X, Zhang L (2020) Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study, Lancet. 395, 507–513. https://doi.org/10.1016/S0140-6736(20)30211-7

  7. Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, Zhang L, Fan G, Xu J, Gu X, Cheng Z, Yu T, Xia J, Wei Y, Wu W, Xie X, Yin W, Li H, Liu M, Xiao Y, Gao H, Guo L, Xie J, Wang G, Jiang R, Gao Z, Jin Q, Wang J, Cao B (2020) Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China, Lancet. 395, 497–506. https://doi.org/10.1016/S0140-6736(20)30183-5

  8. Chouat I, Echtioui A, Khemakhem R, Zouch W, Ghorbel M, Ben A (2021) COVID-19 detection in CT and CXR images using deep learning models, Biogerontology. 7. https://doi.org/10.1007/s10522-021-09946-7

  9. Tahir AM, Qiblawey Y, Khandakar A (n.d.) Deep Learning for Reliable Classification of COVID‑19, MERS, and SARS from Chest X-ray Images

  10. Kakodkar P, Kaka N, Baig M (2020) A Comprehensive Literature Review on the Clinical Presentation, and Management of the Pandemic Coronavirus Disease 2019 (COVID-19), Cureus. 2019. https://doi.org/10.7759/cureus.7560.

  11. Lu R, Zhao X, Li J, Niu P, Yang B, Wu H, Wang W, Song H, Huang B, Zhu N, Bi Y, Ma X, Zhan F (2020) Genomic characterisation and epidemiology of 2019 novel coronavirus: implications for virus origins and receptor binding. Lancet 2020:19–21

    Google Scholar 

  12. Tahamtan A, Ardebili A (2020) Real-time RT-PCR in COVID-19 detection: issues affecting the results, Expert Rev. Mol Diagn 20:453–454. https://doi.org/10.1080/14737159.2020.1757437

    Article  Google Scholar 

  13. Salehi S, Abedi A, Balakrishnan S, Gholamrezanezhad A, Coronavirus disease, (2019) (COVID-19): A systematic review of imaging findings in 919 patients. Am J Roentgenol 215(2020):87–93. https://doi.org/10.2214/AJR.20.23034

    Article  Google Scholar 

  14. Fang Y, Pang P (2020) Senivity of Chest CT for COVID.19: Comparasion to RT.PCR, Radiology. 296, 15–17

  15. Shi F, Wang J, Shi J, Wu Z, Wang Q, Tang Z, He K, Shi Y, Shen D (2021) Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation, and Diagnosis for COVID-19. IEEE Rev Biomed Eng 14:4–15. https://doi.org/10.1109/RBME.2020.2987975

    Article  Google Scholar 

  16. Alimadadi A, Aryal S, Manandhar I, Munroe PB, Joe B, Cheng X (2020) Artificial intelligence and machine learning to fight covid-19. Physiol Genomics 52:200–202. https://doi.org/10.1152/physiolgenomics.00029.2020

    Article  Google Scholar 

  17. Kanne JP, Little BP, Chung JH, Elicker BM, Ketai LH (2020) Essentials for radiologists on COVID-19: An update-radiology scientific expert panel. Radiology 296:E113–E114. https://doi.org/10.1148/radiol.2020200527

    Article  Google Scholar 

  18. Xie X, Zhong Z, Zhao W, Zheng C, Wang F, Liu J (2020) Chest CT for Typical 2019-nCoV Pneumonia: Relationship to Negative RT-PCR Testing, J. Clin. Microbiol. 58, 1–5. https://doi.org/10.1016/j.jcv.2020.104384%0Ahttps://doi.org/10.1016/S2213-2600(20)30315-5%0Ahttps://doi.org/10.1016/j.rbmo.2020.06.001%0Ahttps://doi.org/10.1016/j.mayocp.2020.04.004%0Ahttps://doi.org/10.1016/j.cmi.2020.06.019%0A10.1080/1

  19. Shi H, Han X, Jiang N, Cao Y, Alwalid O, Gu J, Fan Y, Zheng C (2020) Radiological findings from 81 patients with COVID-19 pneumonia in Wuhan, China: a descriptive study. Lancet Infect Dis 20:425–434. https://doi.org/10.1016/S1473-3099(20)30086-4

    Article  Google Scholar 

  20. Wang L, Wong A (2003) COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-Ray images. arXiv preprint arXiv:2003.09871

  21. Oh Y, Park S, Ye JC (2020) Deep Learning COVID-19 Features on CXR Using Limited Training Data Sets. IEEE Trans Med Imaging 39:2688–2700. https://doi.org/10.1109/TMI.2020.2993291

    Article  Google Scholar 

  22. Fan DP, Zhou T, Ji GP, Zhou Y, Chen G, Fu H, Shen J, Shao L (2020) Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images. IEEE Trans Med Imaging 39:2626–2637. https://doi.org/10.1109/TMI.2020.2996645

    Article  Google Scholar 

  23. Hemdan EE-D, Shouman MA, Karar ME (2020) COVIDX-Net: A Framework of Deep Learning Classifiers to Diagnose COVID-19 in X-Ray Images. http://arxiv.org/abs/2003.11055

  24. Ahuja S, Panigrahi BK, Dey N, Rajinikanth V, Gandhi TK (2021) Deep transfer learning-based automated detection of COVID-19 from lung CT scan slices. Appl Intell 51:571–585. https://doi.org/10.1007/s10489-020-01826-w

    Article  Google Scholar 

  25. Ardakani AA, Kanafi AR, Acharya UR, Khadem N, Mohammadi A (2020) Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks. Comput Biol Med 121:103795. https://doi.org/10.1016/j.compbiomed.2020.103795

    Article  Google Scholar 

  26. Mahdy LN, Ezzat KA, Elmousalami HH, Ella HA, Hassanien AE (2020) Automatic X-ray COVID-19 Lung Image Classification System based on Multi-Level Thresholding and Support Vector Machine, MedRxiv. 1–8. https://doi.org/10.1101/2020.03.30.20047787

  27. Bakheet S, Al-Hamadi A (2021) Automatic detection of COVID-19 using pruned GLCM-Based texture features and LDCRF classification. Comput Biol Med 137:104781. https://doi.org/10.1016/j.compbiomed.2021.104781

    Article  Google Scholar 

  28. Tawsif ur rehman, COVID-19 Radiography Database | Kaggle, (n.d.). https://www.kaggle.com/tawsifurrahman/covid19-radiography-database. Accessed 22 Dec 2021

  29. Guennouni S, Ahaitouf A, Mansouri A (2015) A comparative study of multiple object detection using haar-like feature selection and local binary patterns in several platforms, Model. Simul. Eng. 2015. https://doi.org/10.1155/2015/948960

  30. Viola P, Jones M (2001) Rapid Object Detection Using a Boosted Cascade of Simple Features, Cvpr 2001. 1, I-511-I–518. http://ieeexplore.ieee.org/document/990517/

  31. Y. Rahman Arif, A. Gautama Putrada, R. Reza Pahlevi, An evaluation of a modified haar-like features based classifier method for face mask detection in the COVID-19 spread prevention, Proceeding - 2021 Int. Symp. Electron. Smart Devices Intell. Syst. Present Futur. Challenges, ISESD 2021. (2021) 0–4. https://doi.org/10.1109/ISESD53023.2021.9501914

  32. Agarwal M, Singhal A (2021) Directional local co-occurrence patterns based on Haar-like filters, Multimed Tools Appl. 1109–1123. https://doi.org/10.1007/s11042-021-11361-6

  33. Arunmozhi A, Park J (2018) Comparison of HOG, LBP and Haar-Like Features for On-Road Vehicle Detection, IEEE Int. Conf. Electro Inf. Technol. 2018-May 362–367. https://doi.org/10.1109/EIT.2018.8500159

  34. Kadir K, Kamaruddin MK, Nasir H, Safie SI, Bakti ZAK (2015) A comparative study between LBP and Haar-like features for Face Detection using OpenCV, 2014 4th Int. Conf. Eng. Technol. Technopreneuship, ICE2T 2014. 2014-Augus 335–339. https://doi.org/10.1109/ICE2T.2014.7006273

  35. Adeshina SO, Ibrahim H, Teoh SS, Hoo SC (2021) Custom face classification model for classroom using haar-like and lbp features with their performance comparisons. Electron 10:1–15. https://doi.org/10.3390/electronics10020102

    Article  Google Scholar 

  36. Mohamed A, Issam A, Mohamed B, Abdellatif B (2015) Real-time Detection of Vehicles Using the Haar-like Features and Artificial Neuron Networks. Procedia Comput Sci 73:24–31. https://doi.org/10.1016/j.procs.2015.12.044

    Article  Google Scholar 

  37. Zhang L, Wang J, An Z (2023) Vehicle recognition algorithm based on Haar-like features and improved Adaboost classifier. J Ambient Intell Humaniz Comput 14(2):807–815

    Article  Google Scholar 

  38. Chen T, Guestrin C (2016) Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, pp 785–794

  39. Paleczek A, Grochala D, Rydosz A (2021) Artificial breath classification using xgboost algorithm for diabetes detection, Sensors. 21. https://doi.org/10.3390/s21124187

  40. Rahman M, Cao Y, Sun X, Li B, Hao Y (2021) Deep pre-trained networks as a feature extractor with XGBoost to detect tuberculosis from chest X-ray. Comput Electr Eng 93:107252. https://doi.org/10.1016/j.compeleceng.2021.107252

    Article  Google Scholar 

  41. Podder P, Bharati S, Mondal MRH, Kose U (2021). Application of machine learning for the diagnosis of COVID-19, Elsevier Inc. https://doi.org/10.1016/b978-0-12-824536-1.00008-3

  42. Panwar H, Gupta PK, Siddiqui MK, Morales-Menendez R, Singh V (2020) Application of deep learning for fast detection of COVID-19 in X-Rays using nCOVnet. Chaos, Solitons Fractals 138:109944. https://doi.org/10.1016/j.chaos.2020.109944

    Article  MathSciNet  Google Scholar 

  43. Echtioui A, Zouch W, Ghorbel M, Mhiri C, Hamam H (2020) Detection Methods of COVID-19. SLAS Technol 25:566–572. https://doi.org/10.1177/2472630320962002

    Article  Google Scholar 

  44. Shukla P, Verma A, Abhishek, Verma S, Kumar M (2020) Interpreting SVM for medical images using Quadtree, Multimed. Tools Appl. 79, 29353–29373. https://doi.org/10.1007/s11042-020-09431-2

  45. Konar D, Panigrahi BK, Bhattacharyya S, Dey N, Jiang R (2021) Auto-diagnosis of covid-19 using lung ct images with semi-supervised shallow learning network. IEEE Access 9:28716–28728. https://doi.org/10.1109/ACCESS.2021.3058854

    Article  Google Scholar 

  46. Shahin OR, Alshammari HH, Taloba AI, Abd El-Aziz RM (2022) Machine learning approach for autonomous detection and classification of COVID-19 virus. Computers and Electrical Engineering 101, 108055

  47. Oğuz Ç, Yağanoğlu M (2022) Detection of COVID-19 using deep learning techniques and classification methods. Inf Process Manage 59(5):103025

    Article  Google Scholar 

  48. Huyut MT (2023) Automatic detection of severely and mildly infected COVID-19 patients with supervised machine learning models. IRBM 44(1):100725

    Article  Google Scholar 

  49. Alvi, Sohaib Bin Khalid, Muhammad Ziad Nayyer, Muhammad Hasan Jamal, Imran Raza, Isabel de la Torre Diez, Carmen Lili Rodriguez Velasco, Jose Manuel Brenosa, and Imran Ashraf. "A lightweight deep learning approach for COVID-19 detection using X-ray images with edge federation." Digital health 9 (2023): 20552076231203604

  50. Solayman, Sanzida, Sk Azmiara Aumi, Chand Sultana Mery, Muktadir Mubassir, and Riasat Khan. Automatic COVID-19 prediction using explainable machine learning techniques. Intl J Cognit Comput Eng 4 (2023): 36–46

  51. Aggarwal A (2002) Light Propagation in Biological tissue using Monte Carlo Simulation. In Recent Advances In Computational Science And Engineering, pp. 19–22

  52. Kumar A (2009) Light propagation through biological tissue: comparison between Monte Carlo simulation and deterministic models. Int J Biomed Eng Technol 2(4):344–351

    Article  Google Scholar 

  53. Basha SH, Anter AM, Hassanien AE, Abdalla A (2023) Hybrid intelligent model for classifying chest X-ray images of COVID-19 patients using genetic algorithm and neutrosophic logic. Soft Computing 27, no. 6, 3427–3442

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Authors and Affiliations

  1. Department of Multimedia Systems, Faculty of Electronics, Telecommunications, and Informatics, Gdańsk University of Technology, Gdańsk, Poland

    Kashif Shaheed

  2. Department of Computer Science, Imam Muhammad Ibn Saud Islamic University (IMSIU), 11432, Riyadh, Saudi Arabia

    Qasiar Abbas

  3. Department of Computational Sciences, Maharaja Ranjit Singh Punjab Technical University, Bathinda, Punjab, 151001, India

    Munish Kumar

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Shaheed, K., Abbas, Q. & Kumar, M. Automatic diagnosis of CoV-19 in CXR images using haar-like feature and XgBoost classifier. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-18330-9

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