Automated diagnosis of COVID stages from lung CT images using statistical features in 2-dimensional flexible analytic wavelet transform

• Autorzy: Patel Rajneesh Kumar , Kashyap Manish

Identyfikatory

DOI

10.1016/j.bbe.2022.06.005

• Języki publikacji: EN

Abstrakty

EN

TheCOVID-19 epidemic has been causing a global problem since December 2019.COVID-19 is highly contagious and spreads rapidly throughout the world. Thus, early detection is essential. The progression of COVID-19 lung illness has been demonstrated to be aided by chest imaging. The respiratory system is the most vulnerable component of the human body to the COVID virus. COVID can be diagnosed promptly and accurately using images from a chest X-ray and a computed tomography scan. CT scans are preferred over X-rays to rule out other pulmonary illnesses, assist venous entry, and pinpoint any new heart problems. The traditional and trending tools are physical, time-inefficient, and not more accurate. Many techniques for detecting COVID utilizing CT scan images have recently been developed, yet none of them can efficiently detect COVID at an early stage. We proposed a two-dimensional Flexible analytical wavelet transform (FAWT) based on a novel technique in this work. This method is decomposed pre-processed images into sub-bands. Then statistical-based relevant features are extracted, and principal component analysis (PCA) is used to identify robust features. After that, robust features are ranked with the help of the Student’s t-value algorithm. Finally, features are applied to Least Square-SVM (RBF) for classification. According to the experimental outcomes, our model beat state-of-the-art approaches for COVID classification. This model attained better classification accuracy of 93.47%, specificity 93.34%, sensitivity 93.6% and F1-score 0.93 using tenfold cross-validation.

Słowa kluczowe

EN

COVID-19 diagnosis; image classification; machine learning; feature extraction; medical imaging; FAWT; image decomposition

PL

diagnoza COVID-19; klasyfikacja obrazu; uczenie maszynowe; ekstrakcja cech; obrazowanie medyczne; FAWT; dekompozycja obrazu

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2022
• Tom: Vol. 42, no. 3
• Strony: 829--841
• Opis fizyczny: Bibliogr. 73 poz., rys., tab., wykr.

Twórcy

autor

Patel Rajneesh Kumar

• Department of ECE, MANIT Bhopal, Maulana Azad National Institute of Technology, Bhopal, India

autor

Kashyap Manish

• Department of ECE, MANIT Bhopal, Maulana Azad National Institute of Technology, Bhopal, India

Bibliografia

• [1] Gao Y et al. Structure of the RNA-dependent RNA polymerase from COVID-19 virus. Science (80-) 2020;368(6492):779–82.
• [2] C. Wang, P. Horby, F. Hayden, G.G.-T. Lancet, and U. 2020, A novel coronavirus outbreak of global health concern, Lancet 2020;395(10223):470–3.
• [3] Bernheim A et al. Chest CT findings in coronavirus disease 2019 (COVID-19): Relationship to duration of infection. Radiology Jun. 2020;295(3):685–91. https://doi.org/10.1148/RADIOL.2020200463.
• [4] D. Singh, V. Kumar, Vaishali, and M. Kaur, Classification of COVID-19 patients from chest CT images using multi-objective differential evolution-based convolutional neural networks, Eur J Clin Microbiol Infect Dis 2020;39(7):1379–89, doi: 10.1007/S10096-020-03901-Z.
• [5] S. Wang, D. Nayak, D. Guttery, X. Zhang, Y.Z.-I. Fusion, and U. 2021, COVID-19 classification by CCSHNet with deep fusion using transfer learning and discriminant correlation analysis, Inf Fusion 2021:68:131–48.
• [6] Gianchandani N, Jaiswal A, Singh D, Kumar V, Kaur M. Rapid COVID-19 diagnosis using ensemble deep transfer learning models from chest radiographic images. J Ambient Intell Humaniz Comput 2020:1–13. https://doi.org/10.1007/S12652-020-02669-6.
• [7] Gaur P, Malaviya V, Gupta A, Bhatia G, Pachori RB, Sharma D. COVID-19 disease identification from chest CT images using empirical wavelet transformation and transfer learning. Biomed Signal Process Control Jan. 2022;71. https://doi.org/10.1016/J.BSPC.2021.103076 103076.
• [8] Kassania SH, Kassanib PH, Wesolowskic MJ, Schneidera KA, Detersa R. Automatic Detection of Coronavirus Disease (COVID-19) in X-ray and CT Images: A Machine Learning Based Approach. Biocybern Biomed Eng Jul. 2021;41(3):867–79. https://doi.org/10.1016/J.BBE.2021.05.013.
• [9] Amyar A, Modzelewski R, Ruan S. Multi-task deep learning based CT imaging analysis for COVID-19: Classification and segmentation. Comput Biol Med 2020;no. 126. https://doi.org/10.1101/2020.04.16.20064709 104307.
• [10] Yasar H, Ceylan M. A novel comparative study for detection of Covid-19 on CT lung images using texture analysis, machine learning, and deep learning methods. Multimed Tools Appl Feb. 2021;80(4):5423–47. https://doi.org/10.1007/S11042-020-09894-3.
• [11] Y. Wu, S. Gao, J. Mei, J. Xu, D. F.-. . . on I. Processing, and U. 2021, Jcs: An explainable covid-19 diagnosis system by joint classification and segmentation, IEEE Trans Image Process 2021;30:3113–26.
• [12] Abraham B, Nair MS. Computer-aided detection of COVID-19 from X-ray images using multi-CNN and Bayesnet classifier. Biocybern Biomed Eng Oct. 2020;40(4):1436–45. https://doi.org/10.1016/J.BBE.2020.08.005.
• [13] P. Chaudhary, R. P.-C. in biology and Medicine, and U. 2021, FBSED based automatic diagnosis of COVID-19 using X-ray and CT images, Comput. Biol. Med., no. 134, p. 104454, 2021.
• [14] Gour M, Jain S. Automated COVID-19 detection from X-ray and CT images with stacked ensemble convolutional neural network. Biocybern Biomed Eng Jan. 2022;42(1):27–41. https://doi.org/10.1016/J.BBE.2021.12.001.
• [15] L. Wang, Z. Lin, A. W.-S. Reports, and U. 2020, Covid-net: A tailored deep convolutional neural network design for detection of covid-19 cases from chest x-ray images, Sci Rep 2020;10(1);1–2.
• [16] A. Ter-Sarkisov, COVID-CT-Mask-Net: prediction of COVID-19 from CT scans using regional features, Appl. Intell., pp. 1–2, 2022, doi: 10.1007/S10489-021-02731-6.
• [17] N. Rashid, M. A. F. Hossain, M. Ali, M. Islam Sukanya, T. Mahmud, and S. A. Fattah, AutoCovNet: Unsupervised feature learning using autoencoder and feature merging for detection of COVID-19 from chest X-ray images, Biocybern Biomed Eng 2021;41(4):1685–1701, doi: 10.1016/J.BBE.2021.09.004.
• [18] N. Ewen, N. K.-2021 I. 18th I. S. On, and U. 2021, Targeted self supervision for classification on a small COVID-19 CT scan dataset, Int. Symp. Biomed. Imaging, pp. 1481–84, 2021.
• [19] Mishra NK, Singh P, Joshi SD. Automated detection of COVID-19 from CT scan using convolutional neural network. Biocybern Biomed Eng Apr. 2021;41(2):572–88. https://doi.org/10.1016/J.BBE.2021.04.006.
• [20] Ali Ahmed SA et al. Comparison and ensemble of 2D and 3D approaches for COVID-19 detection in CT images. Neurocomputing 2022;488:457–69. https://doi.org/10.1016/J.NEUCOM.2022.02.018.
• [21] Islam MR, Nahiduzzaman M. Complex features extraction with deep learning model for the detection of COVID19 from CT scan images using ensemble based machine learning approach. Expert Syst Appl Jun. 2022;195. https://doi.org/10.1016/J.ESWA.2022.116554 116554.
• [22] A. Shamila Ebenezer, S. Deepa Kanmani, M. Sivakumar, and S. Jeba Priya, Effect of image transformation on EfficientNet model for COVID-19 CT image classification, Mater Today Proc 2022;51:2512–19, doi: 10.1016/J.MATPR.2021.12.121.
• [23] D. Singh, V. Kumar, V. Yadav, and M. Kaur, Deep Neural Network-Based Screening Model for COVID-19-Infected Patients Using Chest X-Ray Images, Int J Pattern Recognit Artif Intell 2021;35(3). doi: 10.1142/S0218001421510046.
• [24] Rajpal S, Agarwal M, Rajpal A, Lakhyani N, Saggar A, Kumar N. Cov-elm classifier: an extreme learning machine based identification of covid-19 using chest x-ray images. arXiv Prepr 2020;arXiv:08637.
• [25] P. Chaudhary, R. P.-B. S. P. and Control, and U. 2021, Automatic diagnosis of glaucoma using two-dimensional Fourier-Bessel series expansion based empirical wavelet transform, Biomed. Signal Process. Control, no. 64, p. 102237, 2021.
• [26] Bayram I. An analytic wavelet transform with a flexible time-frequency covering. IEEE Trans signal Process 2012;61(5):1131–42.
• [27] C. Zhang, B. Li, B. Chen, H. Cao, . . . Y. Z.-M. S. and, and U. 2015, Weak fault signature extraction of rotating machinery using flexible analytic wavelet transform, Mech. Syst. Signal Process., no. 64, pp. 162–87, 2015.
• [28] P. Angelov, E. A. S.- MedRxiv, and U. 2020, SARS-CoV-2 CTscan dataset: A large dataset of real patients CT scans for SARS-CoV-2 identification, 2020, doi: 10.1101/2020.04.24.20078584.
• [29] Reza AM, Realization of the contrast limited adaptive histogram equalization (CLAHE) for real-time image enhancement, J VLSI Signal Process Syst Signal Image Video Technol 2004;38(1);35–44, doi: 10.1023/B:VLSI.0000028532.53893.82.
• [30] S. Taran, P. Sharma, V. B.-K.-B. Systems, and U. 2020, Automatic sleep stages classification using optimize flexible analytic wavelet transform, Knowledge-Based Syst 2020;192:105367.
• [31] D. Parashar, D. A.-I. S. Journal, and U. 2020, Automated classification of glaucoma stages using flexible analytic wavelet transform from retinal fundus images, IEEE Sens J 2020;20(1):12885–94.
• [32] C. Sravani, V. Bajaj, S. Taran, A. S.- Irbm, and U. 2020, Flexible analytic wavelet transform based features for physical action identification using sEMG signals, irbm 2020;41(1):18–22.
• [33] M. Sharma, R. Pachori, U. A.-P. R. Letters, and U. 2017, A new approach to characterize epileptic seizures using analytic time-frequency flexible wavelet transform and fractal dimension, Pattern Recognit Lett 2017;94:172–179.
• [34] You Y, Chen W, Li M, Zhang T, Jiang Y, Zheng X. Automatic focal and non-focal EEG detection using entropy-based features from flexible analytic wavelet transform. Biomed Signal Process Control Mar. 2020;57. https://doi.org/10.1016/J.BSPC.2019.101761 101761.
• [35] M. Kumar, R. Pachori, U. A.- Entropy, and U. 2017, ‘‘Automated diagnosis of myocardial infarction ECG signals using sample entropy in flexible analytic wavelet transform framework, mdpi.com, 2017;19(9);488, doi: 10.3390/e19090488.
• [36] K. Singh, A. S.-B. D. M. and Analytics, and U. 2021, Diagnosis of COVID-19 from chest X-ray images using wavelets-based depthwise convolution network, Big Data Min Anal 2021;4(2):84–93.
• [37] Ashokkumar SR, MohanBabu G, Anupallavi S. A KSOM based neural network model for classifying the epilepsy using adjustable analytic wavelet transform. Multimed Tools Appl Apr. 2020;79(15–16):10077–98. https://doi.org/10.1007/S11042-019-7359-0.
• [38] Benhassine NE, Boukaache A, Boudjehem D. Medical image denoising using optimal thresholding of wavelet coefficients with selection of the best decomposition level and mother wavelet. Int J Imaging Syst Technol 2021. https://doi.org/10.1002/IMA.22589.
• [39] Ramteke DS, Pachori RB, Parey A. Automated Gearbox Fault Diagnosis Using Entropy-Based Features in Flexible Analytic Wavelet Transform (FAWT) Domain. J Vib Eng Technol Oct. 2021;9(7):1703–13. https://doi.org/10.1007/S42417-021-00322-W.
• [40] Theodoridis S, Koutroumbas K. Pattern recognition and neural networks. Lect Notes Comput Sci (including Subser Lect Notes Artif Intell Lect Notes Bioinformatics) 2001;2049 LNAI:169–95. https://doi.org/10.1007/3-540-44673-7_8.
• [41] B. Kirar, D. A.-I. I. Processing, and U. 2018, Computer aided diagnosis of glaucoma using discrete and empirical wavelet transform from fundus images, IET Image Process 2018;13(1):73–82.
• [42] A. Nishad, A. Upadhyay, . . . R. P.-F. G., and U. 2019, Automated classification of hand movements using tunable-Q wavelet transform based filter-bank with surface electromyogram signals, Futur. Gener. Comput. Syst., no. 93, pp. 96–110, 2019.
• [43] S. Wold, K. Esbensen, P. G.-C. and intelligent Laboratory, and U. 1987, Principal component analysis, Chemom Intell Lab Syst 1987;2(1-3):37–52.
• [44] N. Kambhatla, T. L.-N. Computation, and U. 1997, Dimension reduction by local principal component analysis, Neural Comput 1997;9(7):493–516.
• [45] R. Gholami, N. F.-H. of N. Computation, and U. 2017, Support vector machine: principles, parameters, and applications, Inhandb. Neural Comput. Press, pp. 515–535, 2017.
• [46] J. Ye, T. X.-A. intelligence and Statistics, and U. 2007, SVM versus least squares SVM, Artif. Intell. Stat., pp. 644–651, 2007.
• [47] Lahmiri S, Dawson DA, Shmuel A. Performance of machine learning methods in diagnosing Parkinson’s disease based on dysphonia measures. Biomed Eng Lett Feb. 2018;8(1):29–39. https://doi.org/10.1007/S13534-017-0051-2.
• [48] R. Sharma, R. Pachori, U. A.- Entropy, and U. 2015, Application of entropy measures on intrinsic mode functions for the automated identification of focal electroencephalogram signals, Entropy 2015;17(2):669–91.
• [49] J. Huang, C. L.-I. T. on knowledge and Data, and U. 2005, Using AUC and accuracy in evaluating learning algorithms, IEEE Trans Knowl Data Eng 2005;17(3): 299–310.
• [50] Y. Pathak, P. Shukla, K. A.-I. T. On, and U. 2020, Deep bidirectional classification model for COVID-19 disease infected patients, IEEE/ACM Trans Comput Biol Bioinforma 2020;18(4):1234–41.
• [51] K. Noronha, U. Acharya, K. Nayak, . . . R. M.-. . . S. P. and, and U. 2014, Automated classification of glaucoma stages using higher order cumulant features, Biomed. Signal Process. Control, no. 10, pp. 174–83, 2014.
• [52] M. Li, B. Y.-P. R. Letters, and U. 2005, 2D-LDA: A statistical linear discriminant analysis for image matrix, Int Conf Signal Process 2004 Proc 2005;26(4):527–32.
• [53] Daubechies I. Ten lectures on wavelets. SIAM; 1992.
• [54] J. G.-I. transactions on signal processing and U. 2013, Empirical wavelet transform, IEEE Trans Signal Process 2013;61(16):3999–4010.
• [55] Candès E, Demanet L, Donoho D, Ying L. Fast discrete curvelet transforms. Multiscale Model Simul Sep. 2006;5(3):861–99. https://doi.org/10.1137/05064182X.
• [56] M. Do, M. V.-I. T. on image Processing, and U. 2005, The contourlet transform: an efficient directional multiresolution image representation, IEEE Trans Image Process 20005;14(2):2091–106.
• [57] D. Nayak, R. Dash, B. M.- Neurocomputing, and U. 2016, Brain MR image classification using two-dimensional discrete wavelet transform and AdaBoost with random forests, Neurocomputing 2016;177:188–97.
• [58] Pal M. Random forest classifier for remote sensing classification. Int J Remote Sens Jan. 2005;26(1):217–22. https://doi.org/10.1080/01431160412331269698.
• [59] Zhang ML, Zhou ZH. ML-KNN: A lazy learning approach to multi-label learning. Pattern Recognit Jul. 2007;40(7):2038–48. https://doi.org/10.1016/J.PATCOG.2006.12.019.
• [60] Adankon MM, Cheriet M. Model selection for the LS-SVM. Application to handwriting recognition. Pattern Recognit Dec. 2009;42(12):3264–70. https://doi.org/10.1016/J.PATCOG.2008.10.023.
• [61] S. Lahmiri, A. S.-B. S. P. and Control, and U. 2019, Detection of Parkinson’s disease based on voice patterns ranking and optimized support vector machine, Biomed. Signal Process. Control, no. 49, pp. 427–33, 2019.
• [62] X. Yang, X. He, J. Zhao, Y. Zhang, . . . S. Z. preprint arXiv, and U. 2020, COVID-CT-dataset: a CT scan dataset about COVID-19, arxiv, p. 13865, 2020.
• [63] Di D et al. Hypergraph learning for identification of COVID-19 with CT imaging. Med Image Anal Feb. 2021;68. https://doi.org/10.1016/j.media.2020.101910.
• [64] Z. Wang, Q. Liu, Q. D.-I. J. of B. and Health, and U. 2020, Contrastive cross-site learning with redesigned net for covid-19 ct classification, IEEE J Biomed Heal Informatics 2020:24(10):2806–13.
• [65] Maheshwari S, . . . R. P.-I. journal of, and U. 2016, Automated diagnosis of glaucoma using empirical wavelet transform and correntropy features extracted from fundus images, IEEE J Biomed Heal Informatics 2016;21(3):803–13.
• [66] Acharya U, Sree S, Krishnan M, . . . F. M.-U. In medicine, and U. 2012, Atherosclerotic risk stratification strategy for carotid arteries using texture-based features, Ultrasound Med Biol 2012;38(6):899–915.
• [67] Ali Z et al. iSCAN: An RT-LAMP-coupled CRISPR-Cas12 module for rapid, sensitive detection of SARS-CoV-2. Virus Res 2020;288. https://doi.org/10.1016/J.VIRUSRES.2020.198129198129.
• [68] Burghouts GJ, Geusebroek JM. Material-specific adaptation of color invariant features. Pattern Recognit Lett 2009;30(3):306–13. https://doi.org/10.1016/J.PATREC.2008.10.005.
• [69] Acharya UR et al. Automated characterization of fatty liver disease and cirrhosis using curvelet transform and entropy features extracted from ultrasound images. Comput Biol Med 2016;79:250–8. https://doi.org/10.1016/J.COMPBIOMED.2016.10.022.
• [70] Mohammed M, Abdulkareem K, . . . A. A.-W.-I., U. 2020, Benchmarking methodology for selection of optimal COVID-19 diagnostic model based on entropy and TOPSIS methods, IEEE Access 2020;8:99115–31, 2020.
• [71] Wang S et al. A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19). Eur Radiol 2021;31(8):6096–104. https://doi.org/10.1007/S00330-021-07715-1.
• [72] Yan T, Wong PK, Ren H, Wang H, Wang J, Li Y. Automatic distinction between COVID-19 and common pneumonia using multi-scale convolutional neural network on chest CT scans. Chaos, Solitons Fractals Nov. 2020;140. https://doi.org/10.1016/J.CHAOS.2020.110153 110153.
• [73] Hasan NI. A hybrid method of covid-19 patient detection from modified CT-scan/chest-X-ray images combining deep convolutional neural network and two-dimensional empirical mode decomposition. Comput Methods Programs Biomed Updat 2021;1. https://doi.org/10.1016/J.CMPBUP.2021.100022 100022.