COVID-19 Diagnosis by Wavelet Entropy and Extreme Learning Machine


  • Xue Han Nanjing Normal University of Special Education, China
  • Zuojin Hu Nanjing Normal University of Special Education, China
  • William Wang Waynesburg University image/svg+xml



COVID-19, diagnosis, Wavelet Entropy, Extreme Learning Machine, k-fold cross validation


In recent years, COVID-19 has spread rapidly among humans. Chest CT is an effective means of diagnosing COVID-19. However, the diagnosis of CT images still depends on the doctor's visual judgment and medical experience. This takes a certain amount of time and may lead to misjudgment. In this paper, a new algorithm for automatic diagnosis of COVID-19 based on chest CT image data was proposed. The algorithm comprehensively uses WE to extract image features, uses ELM for training, and finally passes k-fold CV validation. After evaluating and detecting performance on 296 chest CT images, our proposed method is superior to state-of-the-art approaches in terms of sensitivity, specificity, precision, accuracy, F1, MCC and FMI. 


Kovacs, K.D., Determination of the human impact on the drop in NO2 air pollution due to total COVID-19 lockdown using Human-Influenced Air Pollution Decrease Index (HIAPDI)*. Environmental Pollution, 2022. 306: Article ID. 119441

Diaz, A., C. Esparcia, and R. Lopez, The diversifying role of socially responsible investments during the COVID-19 crisis: A risk management and portfolio performance analysis. Economic Analysis and Policy, 2022. 75: p. 39-60

Farhangnia, P., S. Dehrouyeh, A.R. Safdarian, S.V. Farahani, M. Gorgani, N. Rezaei, M. Akbarpour, and A.A. Delbandi, Recent advances in passive immunotherapies for COVID-19: The Evidence-Based approaches and clinical trials. International Immunopharmacology, 2022. 109: Article ID. 108786

J. Wang, Z.X., J. Wang, R. Feng, Y. An, W. Ao, Y. Gao, X. Wang, Z. Xie, CT characteristics of patients infected with 2019 novel coronavirus: association with clinical type. Clinical Radiology, 2020(75): p. 408-414

A. Bernheim, X.M., M. Huang, Y. Yang, Z. A. Fayad, N. Zhang, et al., Chest CT findings in coronavirus disease-19 (COVID-19): relationship to duration of infection. Radiology, 2020: p. 295

Ong, S., C. Pascoe, Z. Ballock, S. Sengupta, D. Murphy, and N. Lawrentschuk, Long Term follow-up of men having a PSMA PET-CT for biochemical progression of prostate cancer - Is a negative scan prognostic? BJU International, 2022. 129: p. 155-155

Sahba, S., A. Huurnink, J.M. van den Berg, B. Tuitert, S.J. Vastert, and G.W. ten Tusscher, Systemic Juvenile Idiopathic Arthritis in two children; case report on clinical course, challenges in diagnosis and the role of FDG-PET/CT-scan. Clinical Case Reports, 2022. 10(6): Article ID. e05900

Halily, S., B. Abdulhakeem, Y. Oukessou, S. Rouadi, R. Abada, M. Roubal, and M. Mahtar, CT scan findings impact on hearing thresholds in otosclerosis: A study of 108 patients. Annals of Medicine and Surgery, 2022. 77: Article ID. 103716

Das, S.S., D. Malik, G. Khanna, I.B. Sen, and R. Patir, F-18-Labeled WBC PET/CT Scan in a Case of Recurrent Glioblastoma Multiforme, Presented as Pyrexia of Unknown Origin. Clinical Nuclear Medicine, 2022. 47(7): p. E500-E502

Ingvardson, G.T., D. Muter, and B.P. Foley, Purse of medieval silver coins from royal shipwreck revealed by X-ray microscale Computed Tomography (?CT) scanning. Journal of Archaeological Science-Reports, 2022. 43: Article ID. 103468

Watt, I., E. Holden, F. Waldie, J. Bhattacharya, R. Devin, and J. Wu, Utility of CT head scan post inpatient fall in older adults. Australasian Journal on Ageing, 2022. 41: p. 25-25

Zhihai Lu, S.L., Ge Liu, Yudong Zhang, Jianfei Yang, Preetha Phillips, A Pathological Brain Detection System Based on Radial Basis Function Neural Network. Journal of Medical Imaging and Health Informatics, 2016. 6(5): p. 1218-1222

Siyuan Lu, Z.L., Jianfei Yang, Ming Yang, Shuihua Wang, A pathological brain detection system based on kernel based ELM. Multimedia Tools and Applications, 2018. 77(3): p. 3715-3728

Siyuan Lu, X.Q., Jianpin Shi, Na Li, Zhi-Hai Lu, Peng Chen, Meng-Meng Yang, Fang-Yuan Liu, Wen-Juan Jia, Yudong Zhang, A Pathological Brain Detection System based on Extreme Learning Machine Optimized by Bat Algorithm. CNS & Neurological Disorders - Drug Targets, 2017. 16(1): p. 23-29

Ahmadipour, M., M.M. Othman, M. Alrifaey, R. Bo, and C.K. Ang, Classification of faults in grid-connected photovoltaic system based on wavelet packet transform and an equilibrium optimization algorithm-extreme learning machine. Measurement, 2022. 197: Article ID. 111338

Choi, S. and D. Harrison, Combined 7-Tesla MRI and clinical features in Random Forests and eXtreme Gradient Boost machine learning algorithms for MS progression status classification. Multiple Sclerosis Journal, 2022. 28(1_SUPPL): p. 137-138

Zhang, Y.-D., A five-layer deep convolutional neural network with stochastic pooling for chest CT-based COVID-19 diagnosis. Machine Vision and Applications, 2021. 32: Article ID. 14

Wang S , W.X., Zhang Y D , et al., Diagnosis of COVID-19 by Wavelet Renyi Entropy and Three-Segment Biogeography-Based Optimization. International Journal of Computational Intelligence Systems, 2020(13(1)): p. 1332-1344

Liu, G., Computer-aided diagnosis of abnormal breasts in mammogram images by weighted-type fractional Fourier transform. Advances in Mechanical Engineering, 2016. 8(2): Article ID. 11

Hussain, N., M. Hasanzade, and D.W. Breiby, Performance comparison of wavelet families for noise reduction and intensity thresholding in Fourier Ptychographic microscopy. Optics Communications, 2022. 519: Article ID. 128400

Chatterjee, S., Sparsity-based modified wavelet de-noising autoencoder for ECG signals. Signal Processing, 2022. 198: Article ID. 108605

Zhang, Y.D., L.N. Wu, G. Wei, and S.H. Wang, A novel algorithm for all pairs shortest path problem based on matrix multiplication and pulse coupled neural network. Digital Signal Processing, 2011. 21(4): p. 517-521

Hou, X.-X., Alcoholism detection by medical robots based on Hu moment invariants and predator-prey adaptive-inertia chaotic particle swarm optimization. Computers and Electrical Engineering, 2017. 63: p. 126-138

Li, P. and G. Liu, Pathological Brain Detection via Wavelet Packet Tsallis Entropy and Real-Coded Biogeography-based Optimization. Fundamenta Informaticae, 2017. 151(1-4): p. 275-291

Valogiannis, G. and C. Dvorkin, Towards an optimal estimation of cosmological parameters with the wavelet scattering transform. Physical Review D, 2022. 105(10): Article ID. 103534

Takano, D., T. Minamoto, and Ieee. Feature extraction method for early-stage colorectal cancer using dual-tree complex wavelet packet transform. in International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR). 2021. Electr Network: IEEE. p. 1-4

Hiramatsu, T., T. Manamoto, and Ieee. Detection of maliciously blurred image portions using dyadic wavelet transform and jensen-shannon divergence. in International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR). 2021. Electr Network. p. 5-10

Yamni, M., H. Karmouni, M. Sayyouri, and H. Qjidaa, Robust audio watermarking scheme based on fractional Charlier moment transform and dual tree complex wavelet transform. Expert Systems with Applications, 2022. 203: Article ID. 117325

Wang, S.-H., DenseNet-201-Based Deep Neural Network with Composite Learning Factor and Precomputation for Multiple Sclerosis Classification. ACM Trans. Multimedia Comput. Commun. Appl., 2020. 16(2s): p. Article 60

Ganesan, A. and S.M. Santhanam, A novel feature descriptor based coral image classification using extreme learning machine with ameliorated chimp optimization algorithm. Ecological Informatics, 2022. 68: Article ID. 101527

Ghoggali, N., F. Douak, and W. Ghoggali, Towards a NIR Spectroscopy ensemble learning technique competing with the standard ASTM-CFR: An optimal boosting and bagging extreme learning machine algorithms for gasoline octane number prediction. Optik, 2022. 257: Article ID. 168813

Lu, S., Cerebral Microbleed Detection via Convolutional Neural Network and Extreme Learning Machine. Frontiers in Computational Neuroscience, 2021. 15: Article ID. 738885

Gutierrez, D.A., F.S. Lasheras, V.M. Sanchez, S.L.S. Gomez, V. Moreno, F. Moratalla-Navarro, and A.J.M. de la Torre, A New Algorithm for Multivariate Genome Wide Association Studies Based on Differential Evolution and Extreme Learning Machines. Mathematics, 2022. 10(7): Article ID. 1024

Zhang, Y.-D., G. Zhao, J. Sun, X. Wu, Z.-H. Wang, H.-M. Liu, V.V. Govindaraj, T. Zhan, and J. Li, Smart pathological brain detection by synthetic minority oversampling technique, extreme learning machine, and Jaya algorithm. Multimedia Tools and Applications, 2017. 77(17): p. 22629-22648

Wei, G., Color Image Enhancement based on HVS and PCNN. SCIENCE CHINA Information Sciences, 2010. 53(10): p. 1963-1976

Guang-Bin Huang, Q.-Y.Z., Chee-Kheong Siew, Extreme learning machine: Theory and applications. Neurocomputing, 2006(70): p. 489-501

Wu, X., Smart detection on abnormal breasts in digital mammography based on contrast-limited adaptive histogram equalization and chaotic adaptive real-coded biogeography-based optimization. Simulation, 2016. 92(9): p. 873-885

Bartlett, P.L., The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network. IEEE Trans. Inf. Theory, 1998(44(2)): p. 525-536

Nikzad, S. and A. Ebrahimi, Two person interaction recognition based on a dual-coded modified metacognitive (DCMMC) extreme learning machine. Turkish Journal of Electrical Engineering and Computer Sciences, 2022. 30(4): p. 1621-1636

Khan, A.R., T. Saba, T. Sadad, and S.P. Hong, Cloud-Based Framework for COVID-19 Detection through Feature Fusion with Bootstrap Aggregated Extreme Learning Machine. Discrete Dynamics in Nature and Society, 2022. 2022: Article ID. 3111200

Akbarian, S., C.Y. Xu, W.J. Wang, S. Ginns, and S. Lim, Sugarcane yields prediction at the row level using a novel cross-validation approach to multi-year multispectral images. Computers and Electronics in Agriculture, 2022. 198: Article ID. 107024

de Bruin, S., D.J. Brus, G.B.M. Heuvelink, T.V. Tengbergen, and A. Wadoux, Dealing with clustered samples for assessing map accuracy by cross-validation. Ecological Informatics, 2022. 69: Article ID. 101665

Li, Y., Detection of Dendritic Spines using Wavelet Packet Entropy and Fuzzy Support Vector Machine. CNS & Neurological Disorders - Drug Targets, 2017. 16(2): p. 116-121

Soper, D.S., Greed Is Good: Rapid Hyperparameter Optimization and Model Selection Using Greedy k-Fold Cross Validation. Electronics, 2021. 10(16): p. 1973

Peng, B., Y.-X. Liang, J. Yang, and K. So, Image processing methods to elucidate spatial characteristics of retinal microglia after optic nerve transection. Scientific Reports, 2016. 6: Article ID. 21816

Chaudhary, F.A., A. Iqbal, M.D. Khalid, N. Noor, J. Syed, M.N. Baig, O. Khattak, and S.U. Din, Validation and Reliability Testing of the Child Oral Impacts on Daily Performances (C-OIDP): Cross-Cultural Adaptation and Psychometric Properties in Pakistani School-Going Children. Children-Basel, 2022. 9(5): Article ID. 631

Kuppusamy, Y., R. Jayaseelan, G. Pandulu, V.S. Kumar, G. Murali, S. Dixit, and N.I. Vatin, Artificial Neural Network with a Cross-Validation Technique to Predict the Material Design of Eco-Friendly Engineered Geopolymer Composites. Materials, 2022. 15(10): Article ID. 3443

Minhas, H., A. Malik, D. Kurtz, Z. Fatiwala, F. Ahmed, F. Irfan, S. Lee, and Z. Esber, Cross-Validation of a Global Machine Learning Model to Predict COVID-19 Mortality. American Journal of Respiratory and Critical Care Medicine, 2022. 205

Ramirez, J., Unilateral sensorineural hearing loss identification based on double-density dual-tree complex wavelet transform and multinomial logistic regression. Integrated Computer-Aided Engineering, 2019. 26: p. 411-426




How to Cite

X. Han, Z. Hu, and W. Wang, “COVID-19 Diagnosis by Wavelet Entropy and Extreme Learning Machine”, EAI Endorsed Trans e-Learn, vol. 8, no. 1, p. e3, Aug. 2022.