COVID‑19 Infection Detection from Chest X‑Ray Images Using Feature Fusion and Machine Learning

Faraidoon Hassan  Ahmad; Shakhawan Haris  Wady

doi:10.25098/5.2.16

Authors

Faraidoon Hassan Ahmad Department of Pharmacognosy and Pharmaceutical Chemistry, College of Pharmacy, University of Sulaimani, Sulaimani, Iraq, Department of Information Technology, University College of Goizha, Sulaimani, Iraq
Shakhawan Hares Wady Applied Computer, College of Medicals and Applied Sciences, Charmo University, Chamchamal, Sulaimani, Iraq, Department of Information Technology, University College of Goizha, Sulaimani, Iraq

DOI:

https://doi.org/10.25098/5.2.16

Keywords:

COVID-19 diagnosis, Chest X-rays, Feature Extraction, Machine learning

Abstract

COVID-19 is a severe viral infection that poses a serious threat on humanity as a whole; it has affected almost all aspects of life. To overcome the threat, experts use different methods to detect the infection of COVID-19. One of the main techniques is the use of medical images which provides experts with valuable information to accurately detect the infection. Many researches have concentrated on automation of COVID-19 classification using artificial intelligence techniques on chest X-ray (CXR) images. This paper concentrated on designing and developing an intelligent pipeline for the COVID-19 identification by fusing the features extracted using Curvelet Transform (CT), Gabor Wavelet Transform (GWT), and Local Gradient Increasing Pattern (LGIP), then to classify the CXR images, the images were fed into four machine learning classifiers, Discriminant Analysis (DISC), Ensemble, Random Forest (RF), and Support Vector Machine (SVM). To verify the validity of the proposed model performance, a total of 7232 CXR healthy and COVID-19 images were used which were obtained from a COVID-19 Radiography database. Experimental results indicated that the proposed feature fusion technique assured a satisfactory performance in terms of identifying COVID-19 compared to other state-of-the-art works with overall testing accuracy of 96.18%, precision of 95.46%, sensitivity of 96.98%, and F1-score of 96.21% using SVM classifier.

References

Pham TD. Classification of COVID-19 chest X-rays with deep learning: new models or fine tuning? Heal Inf Sci Syst 2021;9. https://doi.org/10.1007/s13755-020-00135-3.

Rahman T, Khandakar A, Qiblawey Y, Tahir A, Kiranyaz S. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID- 19 . The COVID-19 resource centre is hosted on Elsevier Connect , the company ’ s public news and information 2020.

Wang D, Hu B, Hu C, Zhu F, Liu X, Zhang J, et al. Clinical Characteristics of 138 Hospitalized Patients with 2019 Novel Coronavirus-Infected Pneumonia in Wuhan, China. JAMA - J Am Med Assoc 2020;323:1061–9. https://doi.org/10.1001/jama.2020.1585.

Apostolopoulos ID, Mpesiana TA. Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks. Phys Eng Sci Med 2020;43:635–40. https://doi.org/10.1007/s13246-020-00865-4.

Abbas A, Abdelsamea MM, Gaber MM. Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network. Appl Intell 2021;51:854–64. https://doi.org/10.1007/s10489-020-01829-7.

Aradhya VNM, Mahmud M, Guru DS, Agarwal B, Kaiser MS. One-shot Cluster-Based Approach for the Detection of COVID–19 from Chest X–ray Images. Cognit Comput 2021;13:873–81. https://doi.org/10.1007/s12559-020-09774-w.

Narin A, Kaya C, Pamuk Z. Automatic Detection of Coronavirus Disease (COVID-19) Using X-ray Images and Deep Convolutional Neural Networks. 2020.

Ahmad F, Farooq A, Ghani MU. Deep Ensemble Model for Classification of Novel Coronavirus in Chest X-Ray Images. Comput Intell Neurosci 2021;2021. https://doi.org/10.1155/2021/8890226.

Turkoglu M. COVIDetectioNet: COVID-19 diagnosis system based on X-ray images using features selected from pre-learned deep features ensemble. Appl Intell 2021;51:1213–26. https://doi.org/10.1007/s10489-020-01888-w.

Al-Waisy AS, Mohammed MA, Al-Fahdawi S, Maashi MS, Garcia-Zapirain B, Abdulkareem KH, et al. COVID-DeepNet: Hybrid Multimodal Deep Learning System for Improving COVID-19 Pneumonia Detection in Chest X-ray Images. Comput Mater Contin 2021;67:2409–29. https://doi.org/10.32604/cmc.2021.012955.

Punn NS, Agarwal S. Automated diagnosis of COVID-19 with limited posteroanterior chest X-ray images using fine-tuned deep neural networks. Appl Intell 2021;51:2689–702. https://doi.org/10.1007/s10489-020-01900-3.

Zargari Khuzani A, Heidari M, Shariati SA. COVID-Classifier: an automated machine learning model to assist in the diagnosis of COVID-19 infection in chest X-ray images. Sci Rep 2021;11:1–6. https://doi.org/10.1038/s41598-021-88807-2.

López-Cabrera JD, Orozco-Morales R, Portal-Diaz JA, Lovelle-Enríquez O, Pérez-Díaz M. Current limitations to identify COVID-19 using artificial intelligence with chest X-ray imaging. Health Technol (Berl) 2021;11:411–24. https://doi.org/10.1007/s12553-021-00520-2.

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;24:1207–20. https://doi.org/10.1007/s10044-021-00984-y.

Singh AK, Kumar A, Mahmud M, Kaiser MS, Kishore A. COVID-19 Infection Detection from Chest X-Ray Images Using Hybrid Social Group Optimization and Support Vector Classifier. Cognit Comput 2021. https://doi.org/10.1007/s12559-021-09848-3.

Jain R, Gupta M, Taneja S, Hemanth DJ. Deep learning based detection and analysis of COVID-19 on chest X-ray images 2020.

Karthik R, Menaka R, Hariharan M. Learning distinctive filters for COVID-19 detection from chest X-ray using shuffled residual CNN. Appl Soft Comput 2021;99:106744. https://doi.org/10.1016/j.asoc.2020.106744.

Nur-a-alam, Ahsan M, Based MA, Haider J, Kowalski M. COVID-19 detection from chest X-ray images using feature fusion and deep learning. Sensors 2021;21:1–30. https://doi.org/10.3390/s21041480.

Bourouis S, Alharbi A, Bouguila N. Bayesian Learning of Shifted-Scaled Dirichlet Mixture Models and Its Application to Early COVID-19 Detection in Chest X-ray Images. J Imaging 2021;7:7. https://doi.org/10.3390/jimaging7010007.

Ohata EF, Bezerra GM, Victor J, Vieira A, Neto L, Albuquerque B, et al. Automatic Detection of COVID-19 Infection Using Chest X-Ray Images Through Transfer Learning 2021;8. https://doi.org/10.1109/JAS.2020.1003393.

Zulfaezal M, Azemin C, Hassan R, Izzuddin M, Tamrin M, Ali MA. COVID-19 Deep Learning Prediction Model Using Publicly Available Radiologist-Adjudicated Chest X-Ray Images as Training Data : Preliminary Findings 2020;2020.

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;5:643–51. https://doi.org/10.33889/IJMEMS.2020.5.4.052.

Keidar D, Yaron D, Goldstein E, Shachar Y, Blass A, Charbinsky L, et al. COVID-19 classification of X-ray images using deep neural networks. Eur Radiol 2021. https://doi.org/10.1007/s00330-021-08050-1.

Elaziz MA, Hosny KM, Salah A, Darwish MM, Lu S, Sahlol AT. New machine learning method for imagebased diagnosis of COVID-19. PLoS One 2020;15:1–13. https://doi.org/10.1371/journal.pone.0235187.

Rasheed J, Hameed AA, Djeddi C, Jamil A, Al-Turjman F. A machine learning-based framework for diagnosis of COVID-19 from chest X-ray images. Interdiscip Sci Comput Life Sci 2021;13:103–17. https://doi.org/10.1007/s12539-020-00403-6.

Hira S, Bai A, Hira S. An automatic approach based on CNN architecture to detect Covid-19 disease from chest X-ray images. Appl Intell 2021;51:2864–89. https://doi.org/10.1007/s10489-020-02010-w.

Al-antari MA, Hua CH, Bang J, Lee S. “Fast deep learning computer-aided diagnosis of COVID-19 based on digital chest x-ray images.” Appl Intell 2021;51:2890–907. https://doi.org/10.1007/s10489-020-02076-6.

Afifi A, Hafsa NE, Ali MAS, Alhumam A, Alsalman S. An ensemble of global and local-attention based convolutional neural networks for COVID-19 diagnosis on chest X-ray images. Symmetry (Basel) 2021;13:1–25. https://doi.org/10.3390/sym13010113.

Chowdhury MEH, Rahman T, Khandakar A, Mazhar R, Kadir MA, Mahbub ZB, et al. Can AI Help in Screening Viral and COVID-19 Pneumonia? IEEE Access 2020;8:132665–76. https://doi.org/10.1109/ACCESS.2020.3010287.

Rahman T, Khandakar A, Qiblawey Y, Tahir A, Kiranyaz S, Abul Kashem S Bin, et al. Exploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images. Comput Biol Med 2021;132:104319. https://doi.org/https://doi.org/10.1016/j.compbiomed.2021.104319.

Wady SH, Yousif RZ, Hasan HR. A Novel Intelligent System for Brain Tumor Diagnosis Based on a Composite Neutrosophic-Slantlet Transform Domain for Statistical Texture Feature Extraction. Biomed Res Int 2020;2020:8125392. https://doi.org/10.1155/2020/8125392.

Candès E, Demanet L, Donoho D, Ying L. Fast Discrete Curvelet Transforms. SIAM J Multiscale Model Simul 2006;5. https://doi.org/10.1137/05064182X.

Hashim A. Image Denoising Based Improved Curvelet Threshold 2021.

Lahmiri S, Boukadoum M. Hybrid discrete wavelet transform and Gabor filter banks processing for mammogram features extraction. 2011 IEEE 9th Int New Circuits Syst Conf NEWCAS 2011 2011;2013:53–6. https://doi.org/10.1109/NEWCAS.2011.5981217.

Zhou L, Wang H. Local gradient increasing pattern for facial expression recognition. Proc. - Int. Conf. Image Process. ICIP, 2012, p. 2601–4. https://doi.org/10.1109/ICIP.2012.6467431.

Lubing Z, Han W. Local gradient increasing pattern for facial expression recognition. 2012. https://doi.org/10.1109/ICIP.2012.6467431.

Ahmed S. Ethnicity Identification based on Fusion Strategy of Local and Global Features Extraction. International Journal of Multidisciplinary and Current Research, Vol. 4, 2016, No 2, pp. 200-205.