Unsupervised deep learning based variational autoencoder model for COVID-19 diagnosis and classification

At present times, COVID-19 has become a global illness and infected people has increased exponentially and it is difficult to control due to the non-availability of large quantity of testing kits. Artificial intelligence (AI) techniques including machine learning (ML), deep learning (DL), and comput...

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Autores:: Mansour, Romany F.
Escorcia-Gutierrez, Jose
Gamarra, Margarita
Gupta, Deepak
Castillo, Oscar
kumar, sachin

Tipo de recurso:: http://purl.org/coar/resource_type/c_816b

Fecha de publicación:: 2021

Institución:: Corporación Universidad de la Costa

Repositorio:: REDICUC - Repositorio CUC

Idioma:: eng

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network_acronym_str	RCUC2
network_name_str	REDICUC - Repositorio CUC
repository_id_str
dc.title.spa.fl_str_mv	Unsupervised deep learning based variational autoencoder model for COVID-19 diagnosis and classification
title	Unsupervised deep learning based variational autoencoder model for COVID-19 diagnosis and classification
spellingShingle	Unsupervised deep learning based variational autoencoder model for COVID-19 diagnosis and classification COVID-19 Deep learning Unsupervised learning Variational autoencoder Image classification
title_short	Unsupervised deep learning based variational autoencoder model for COVID-19 diagnosis and classification
title_full	Unsupervised deep learning based variational autoencoder model for COVID-19 diagnosis and classification
title_fullStr	Unsupervised deep learning based variational autoencoder model for COVID-19 diagnosis and classification
title_full_unstemmed	Unsupervised deep learning based variational autoencoder model for COVID-19 diagnosis and classification
title_sort	Unsupervised deep learning based variational autoencoder model for COVID-19 diagnosis and classification
dc.creator.fl_str_mv	Mansour, Romany F. Escorcia-Gutierrez, Jose Gamarra, Margarita Gupta, Deepak Castillo, Oscar kumar, sachin
dc.contributor.author.spa.fl_str_mv	Mansour, Romany F. Escorcia-Gutierrez, Jose Gamarra, Margarita Gupta, Deepak Castillo, Oscar kumar, sachin
dc.subject.spa.fl_str_mv	COVID-19 Deep learning Unsupervised learning Variational autoencoder Image classification
topic	COVID-19 Deep learning Unsupervised learning Variational autoencoder Image classification
description	At present times, COVID-19 has become a global illness and infected people has increased exponentially and it is difficult to control due to the non-availability of large quantity of testing kits. Artificial intelligence (AI) techniques including machine learning (ML), deep learning (DL), and computer vision (CV) approaches find useful for the recognition, analysis, and prediction of COVID-19. Several ML and DL techniques are trained to resolve the supervised learning issue. At the same time, the potential measure of the unsupervised learning technique is quite high. Therefore, unsupervised learning techniques can be designed in the existing DL models for proficient COVID-19 prediction. In this view, this paper introduces a novel unsupervised DL based variational autoencoder (UDL-VAE) model for COVID-19 detection and classification. The UDL-VAE model involved adaptive Wiener filtering (AWF) based preprocessing technique to enhance the image quality. Besides, Inception v4 with Adagrad technique is employed as a feature extractor and unsupervised VAE model is applied for the classification process. In order to verify the superior diagnostic performance of the UDL-VAE model, a set of experimentation was carried out to highlight the effective outcome of the UDL-VAE model. The obtained experimental values showcased the effectual results of the UDL-VAE model with the higher accuracy of 0.987 and 0.992 on the binary and multiple classes respectively.
publishDate	2021
dc.date.accessioned.none.fl_str_mv	2021-09-29T19:07:04Z
dc.date.available.none.fl_str_mv	2021-09-29T19:07:04Z
dc.date.issued.none.fl_str_mv	2021
dc.date.embargoEnd.none.fl_str_mv	2023
dc.type.spa.fl_str_mv	Pre-Publicación
dc.type.coar.spa.fl_str_mv	http://purl.org/coar/resource_type/c_816b
dc.type.content.spa.fl_str_mv	Text
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dc.type.version.spa.fl_str_mv	info:eu-repo/semantics/acceptedVersion
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dc.identifier.issn.spa.fl_str_mv	0167-8655
dc.identifier.uri.spa.fl_str_mv	https://hdl.handle.net/11323/8759
dc.identifier.doi.spa.fl_str_mv	https://doi.org/10.1016/j.patrec.2021.08.018
dc.identifier.instname.spa.fl_str_mv	Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv	REDICUC - Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv	https://repositorio.cuc.edu.co/
identifier_str_mv	0167-8655 Corporación Universidad de la Costa REDICUC - Repositorio CUC
url	https://hdl.handle.net/11323/8759 https://doi.org/10.1016/j.patrec.2021.08.018 https://repositorio.cuc.edu.co/
dc.language.iso.none.fl_str_mv	eng
language	eng
dc.relation.references.spa.fl_str_mv	[1] A Krizhevsky, I Sutskever, GE Hinton, ImageNet classification with deep convolutional neural networks, Commun ACM 60 (6) (2017) 84–90 May. [2] D.A. Pustokhin, I.V. Pustokhina, P.N. Dinh, S.V. Phan, G.N. Nguyen, G.P. Joshi, An effective deep residual network based class attention layer with bidirectional LSTM for diagnosis and classification of COVID-19, Journal of Applied Statistics (2020) 1–18. [3] D.N. Le, V.S. Parvathy, D. Gupta, A. Khanna, J.J. Rodrigues, K. Shankar, IoT enabled depthwise separable convolution neural network with deep support vector machine for COVID-19 diagnosis and classification, International Journal of Machine Learning and Cybernetics (2021) 1–14. [4] L. Sorensen, M. Loog, P. Lo, H. Ashraf, A. Dirksen, R.P. Duin, M. De Bruijne, Image dissimilarity-based quantification of lung disease from CT, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, Berlin, Heidelberg, 2010, pp. 37–44. [5] W.L. Zhang, X.Z. Wang, Feature extraction and classification for human brain CT images, in: In 2007 International Conference on Machine Learning and Cybernetics, 2, IEEE, 2007, pp. 1155–1159. [6] M.R.P. Homem, N.D.A. Mascarenhas, P.E. Cruvinel, The linear attenuation coefficients as features of multiple energy CT image classification, Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 452 (1-2) (2000) 351–360. [7] A. Albrecht, E. Hein, K. Steinhˆfel, M. Taupitz, C.K. Wong, Bounded-depth threshold circuits for computer-assisted CT image classification, Artificial Intelligence in Medicine 24 (2) (2002) 179–192. [8] X. Yang, I. Sechopoulos, B. Fei, Automatic tissue classification for high-resolution breast CT images based on bilateral filtering, In Medical Imaging 2011: Image Processing 7962 (2011) 79623H International Society for Optics and Photonics. [9] F. Ozyurt, T. Tuncer, E. Avci, M. Koc, I. Serhatlioglu, A novel liver image classification method using perceptual hash-based convolutional neural network, Arabian Journal for Science and Engineering 44 (4) (2019) 3173–3182. [10] G. Xu, H. Cao, J.K. Udupa, C. Yue, Y. Dong, L. Cao, D.A. Torigian, A novel exponential loss function for pathological lymph node image classification, In MIPPR 2019: Parallel Processing of Images and Optimization Techniques; and Medical Imaging 11431 (2020) 114310A International Society for Optics and Photonics. [11] S.K. Lakshmanaprabu, S.N. Mohanty, K. Shankar, N. Arunkumar, G. Ramirez, Optimal deep learning model for classification of lung cancer on CT images, Future Generation Computer Systems 92 (2019) 374–382. [12] ... & M. Gao, U. Bagci, L. Lu, A. Wu, M. Buty, H.C. Shin, Z Xu, Holistic classification of CT attenuation patterns for interstitial lung diseases via deep convolutional neural networks, Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization 6 (1) (2018) 1–6. [13] Shan, F., Gao, Y., Wang, J., Shi, W., Shi, N., Han, M., Xue, Z., and Shi, Y. Lung Infection Quantification of COVID-19 in CT Images with Deep Learning. arXiv preprint arXiv:2003.04655, 1-19, 2020. [14] Xu, X., Jiang, X., Ma, C., Du, P., Li, X., Lv, S., Yu, L., Chen, Y., Su, J., Lang, G., Li, Y., Zhao, H., Xu, K., Ruan, L., and Wu, W. Deep Learning System to Screen Coronavirus Disease 2019 Pneumonia. arXiv preprint arXiv:2002.09334, 1-29, 2020. [15] Wang, S., Kang, B., Ma, J., Zeng, X., Xiao, M., Guo, J., Cai, M., Yang, J., Li, Y., Meng, X., and Xu, B. A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19). medRxiv preprint doi: https://doi.org/ 10.1101/2020.02.14.20023028, 1-26, 2020. [16] A.MERS-CoV Hamimi, Middle East respiratory syndrome corona virus: Can radiology be of help? Initial single center experience, The Egyptian Journal of Radiology and Nuclear Medicine 47 (1) (2016) 95–106. [17] X. Xie, X. Li, S. Wan, Y. Gong, Mining X-ray images of SARS patients, in: Graham J. Williams, Simeon J. Simoff (Eds.), Data Mining: Theory, Methodology, Techniques, and Applications, Springer-Verlag, Berlin, Heidelberg, 2006, pp. 282–294. ISBN: 3540325476. [18] Pennisi, M., Kavasidis, I., Spampinato, C., Schininà, V., Palazzo, S., Rundo, F., Cristofaro, M., Campioni, P., Pianura, E., Di Stefano, F. and Petrone, A., 2021. An Explainable AI System for Automated COVID-19 Assessment and Lesion Categorization from CT-scans. arXiv preprint arXiv:2101.11943. [19] Turkoglu, M., 2021. COVID-19 Detection System Using Chest CT Images and Multiple Kernels-Extreme Learning Machine Based on Deep Neural Network. IRBM. [20] M. Agarwal, L. Saba, S.K. Gupta, A. Carriero, Z. Falaschi, A. Paschè, P. Danna, A. El-Baz, S. Naidu, J.S. Suri, A Novel Block Imaging Technique Using Nine Artificial Intelligence Models for COVID-19 Disease Classification, Characterization and Severity Measurement in Lung Computed Tomography Scans on an Italian Cohort, Journal of Medical Systems 45 (3) (2021) 1–30. [21] M. Jamshidi, A. Lalbakhsh, J. Talla, Z. Peroutka, F. Hadjilooei, P. Lalbakhsh, M. Jamshidi, L. La Spada, M. Mirmozafari, M. Dehghani, A. Sabet, Artificial intelligence and COVID-19: deep learning approaches for diagnosis and treatment, IEEE Access 8 (2020) 109581–109595. [22] Hemdan, E.E.D., Shouman, M.A. and Karar, M.E., 2020. Covidx-net: A framework of deep learning classifiers to diagnose covid-19 in x-ray images. arXiv preprint arXiv:2003.11055. [23] S.J. Fong, G. Li, N. Dey, R.G. Crespo, E. Herrera-Viedma, Composite Monte Carlo decision making under high uncertainty of novel coronavirus epidemic using hybridized deep learning and fuzzy rule induction, Applied soft computing 93 (2020) 106282. [24] H. Louati, S. Bechikh, A. Louati, C.C. Hung, L.B. Said, Deep Convolutional Neural Network Architecture Design as a Bi-level Optimization Problem, Neurocomputing, 2021. [25] V. Ganesan, P. Rajarajeswari, V. Govindaraj, K.B. Prakash, J. Naren, Post– COVID-19 Emerging Challenges and Predictions on People, Process, and Product by Metaheuristic Deep Learning Algorithm, in: Machine Intelligence and Soft Computing, Springer, Singapore, 2021, pp. 275–287. [26] A. Jaiswal, N. Gianchandani, D. Singh, V. Kumar, M. Kaur, Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning, Journal of Biomolecular Structure and Dynamics (2020) 1–8. [27] M. Alazab, A. Awajan, A. Mesleh, A. Abraham, V. Jatana, S. Alhyari, COVID-19 prediction and detection using deep learning, International Journal of Computer Information Systems and Industrial Management Applications 12 (2020) 168–181. [28] D. Ezzat, A.E. Hassanien, H.A. Ella, An optimized deep learning architecture for the diagnosis of COVID-19 disease based on gravitational search optimization, Applied Soft Computing (2020) 106742. [29] P. Kasinathan, O.D. Montoya, W. Gil-González, R. Arul, M. Moovendan, S. Dhivya, R. Kanimozhi, S. Angalaeswari, APPLICATION OF SOFT COMPUTING TECHNIQUES IN THE ANALYSIS OF COVID–19: A REVIEW, European Journal of Molecular & Clinical Medicine 7 (6) (2020) 2480–2503. [30] A. Altan, S. Karasu, Recognition of COVID-19 disease from X-ray images by hybrid model consisting of 2D curvelet transform, chaotic salp swarm algorithm and deep learning technique, Chaos, Solitons & Fractals 140 (2020) 110071.. [31] R. Kumar, R. Arora, V. Bansal, V.J. Sahayasheela, H. Buckchash, J. Imran, N. Narayanan, G.N. Pandian, B. Raman, Accurate prediction of COVID-19 using chest x-ray images through deep feature learning model with smote and machine learning classifiers, MedRxiv (2020). [32] S. Roy, W. Menapace, S. Oei, B. Luijten, E. Fini, C. Saltori, I. Huijben, N. Chennakeshava, F. Mento, A. Sentelli, E. Peschiera, Deep learning for classification and localization of COVID-19 markers in point-of-care lung ultrasound, IEEE Transactions on Medical Imaging 39 (8) (2020) 2676–2687. [33] T. Zhou, H. Lu, Z. Yang, S. Qiu, B. Huo, Y. Dong, The ensemble deep learning model for novel COVID-19 on CT images, Applied Soft Computing 98 (2021) 106885. [34] M. Nour, Z. Cömert, K. Polat, A novel medical diagnosis model for COVID-19 infection detection based on deep features and Bayesian optimization, Applied Soft Computing 97 (2020) 106580. [35] Ezzat, D. and Ella, H.A., 2020. GSA-DenseNet121-COVID-19: a hybrid deep learning architecture for the diagnosis of COVID-19 disease based on gravitational search optimization algorithm. arXiv preprint arXiv:2004.05084. [36] M.Z. Islam, M.M. Islam, A. Asraf, A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images, Informatics in medicine unlocked 20 (2020) 100412. [37] MM Rahaman, C Li, Y Yao, F Kulwa, MA Rahman, Q Wang, S Qi, F Kong, X Zhu, X. Zhao, Identification of COVID-19 samples from chest X-Ray images using deep learning: A comparison of transfer learning approaches, Journal of X-ray Science and Technology (Preprint) (2020 Jan 1) 1–9. [38] M.K. Nath, A. Kanhe, M. Mishra, A Novel Deep Learning Approach for Classification of COVID-19 Images, in: In 2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA), 2020, pp. 752–757. IEEE. [39] C.F. Westin, H. Knutsson, R. Kikinis, Adaptive image filtering, in: Handbook of Medical Imaging Processing and Analysis, Academic press, 2000, pp. 3208–3212. [40] C Szegedy, S Iofe, V Vanhoucke, AA Alemi, Inception-v4, inception-resnet and the impact of residual connections on learning, in: In Thirty-First AAAI Conference on Artifcial Intelligence, Association for the Advancement of Artifcial Intelligence,USA, 2017, pp. 1–3. [41] M.Y. Sikkandar, B.A. Alrasheadi, N.B. Prakash, G.R. Hemalakshmi, A. Mohanarathinam, K. Shankar, Deep learning based an automated skin lesion segmentation and intelligent classification model, Journal of ambient intelligence and humanized computing (2020) 1–11. [42] COVID-19 Image Data Collection: Prospective Predictions Are the Future Joseph Paul Cohen and Paul Morrison and Lan Dao and Karsten Roth and Tim Q Duong and Marzyeh Ghassemi arXiv:2006.11988, https://github.com/ieee8023/covidchestxray-dataset, 2020
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spelling	Mansour, Romany F.Escorcia-Gutierrez, JoseGamarra, MargaritaGupta, DeepakCastillo, Oscarkumar, sachin2021-09-29T19:07:04Z2021-09-29T19:07:04Z202120230167-8655https://hdl.handle.net/11323/8759https://doi.org/10.1016/j.patrec.2021.08.018Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/At present times, COVID-19 has become a global illness and infected people has increased exponentially and it is difficult to control due to the non-availability of large quantity of testing kits. Artificial intelligence (AI) techniques including machine learning (ML), deep learning (DL), and computer vision (CV) approaches find useful for the recognition, analysis, and prediction of COVID-19. Several ML and DL techniques are trained to resolve the supervised learning issue. At the same time, the potential measure of the unsupervised learning technique is quite high. Therefore, unsupervised learning techniques can be designed in the existing DL models for proficient COVID-19 prediction. In this view, this paper introduces a novel unsupervised DL based variational autoencoder (UDL-VAE) model for COVID-19 detection and classification. The UDL-VAE model involved adaptive Wiener filtering (AWF) based preprocessing technique to enhance the image quality. Besides, Inception v4 with Adagrad technique is employed as a feature extractor and unsupervised VAE model is applied for the classification process. In order to verify the superior diagnostic performance of the UDL-VAE model, a set of experimentation was carried out to highlight the effective outcome of the UDL-VAE model. The obtained experimental values showcased the effectual results of the UDL-VAE model with the higher accuracy of 0.987 and 0.992 on the binary and multiple classes respectively.Mansour, Romany F.Escorcia-Gutierrez, Jose-will be generated-orcid-0000-0003-0518-3187-600Gamarra, Margarita-will be generated-orcid-0000-0003-1834-2984-600Gupta, DeepakCastillo, Oscarkumar, sachin-will be generated-orcid-0000-0002-1136-8009-600application/pdfengCorporación Universidad de la CostaCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Pattern Recognition Lettershttps://www.sciencedirect.com/science/article/pii/S016786552100310XCOVID-19Deep learningUnsupervised learningVariational autoencoderImage classificationUnsupervised deep learning based variational autoencoder model for COVID-19 diagnosis and classificationPre-Publicaciónhttp://purl.org/coar/resource_type/c_816bTextinfo:eu-repo/semantics/preprinthttp://purl.org/redcol/resource_type/ARTOTRinfo:eu-repo/semantics/acceptedVersion[1] A Krizhevsky, I Sutskever, GE Hinton, ImageNet classification with deep convolutional neural networks, Commun ACM 60 (6) (2017) 84–90 May.[2] D.A. Pustokhin, I.V. Pustokhina, P.N. Dinh, S.V. Phan, G.N. Nguyen, G.P. Joshi, An effective deep residual network based class attention layer with bidirectional LSTM for diagnosis and classification of COVID-19, Journal of Applied Statistics (2020) 1–18.[3] D.N. Le, V.S. Parvathy, D. Gupta, A. Khanna, J.J. Rodrigues, K. Shankar, IoT enabled depthwise separable convolution neural network with deep support vector machine for COVID-19 diagnosis and classification, International Journal of Machine Learning and Cybernetics (2021) 1–14.[4] L. Sorensen, M. Loog, P. Lo, H. Ashraf, A. Dirksen, R.P. Duin, M. De Bruijne, Image dissimilarity-based quantification of lung disease from CT, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, Berlin, Heidelberg, 2010, pp. 37–44.[5] W.L. Zhang, X.Z. Wang, Feature extraction and classification for human brain CT images, in: In 2007 International Conference on Machine Learning and Cybernetics, 2, IEEE, 2007, pp. 1155–1159.[6] M.R.P. Homem, N.D.A. Mascarenhas, P.E. Cruvinel, The linear attenuation coefficients as features of multiple energy CT image classification, Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 452 (1-2) (2000) 351–360.[7] A. Albrecht, E. Hein, K. Steinhˆfel, M. Taupitz, C.K. Wong, Bounded-depth threshold circuits for computer-assisted CT image classification, Artificial Intelligence in Medicine 24 (2) (2002) 179–192.[8] X. Yang, I. Sechopoulos, B. Fei, Automatic tissue classification for high-resolution breast CT images based on bilateral filtering, In Medical Imaging 2011: Image Processing 7962 (2011) 79623H International Society for Optics and Photonics.[9] F. Ozyurt, T. Tuncer, E. Avci, M. Koc, I. Serhatlioglu, A novel liver image classification method using perceptual hash-based convolutional neural network, Arabian Journal for Science and Engineering 44 (4) (2019) 3173–3182.[10] G. Xu, H. Cao, J.K. Udupa, C. Yue, Y. Dong, L. Cao, D.A. Torigian, A novel exponential loss function for pathological lymph node image classification, In MIPPR 2019: Parallel Processing of Images and Optimization Techniques; and Medical Imaging 11431 (2020) 114310A International Society for Optics and Photonics.[11] S.K. Lakshmanaprabu, S.N. Mohanty, K. Shankar, N. Arunkumar, G. Ramirez, Optimal deep learning model for classification of lung cancer on CT images, Future Generation Computer Systems 92 (2019) 374–382.[12] ... & M. Gao, U. Bagci, L. Lu, A. Wu, M. Buty, H.C. Shin, Z Xu, Holistic classification of CT attenuation patterns for interstitial lung diseases via deep convolutional neural networks, Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization 6 (1) (2018) 1–6.[13] Shan, F., Gao, Y., Wang, J., Shi, W., Shi, N., Han, M., Xue, Z., and Shi, Y. Lung Infection Quantification of COVID-19 in CT Images with Deep Learning. arXiv preprint arXiv:2003.04655, 1-19, 2020.[14] Xu, X., Jiang, X., Ma, C., Du, P., Li, X., Lv, S., Yu, L., Chen, Y., Su, J., Lang, G., Li, Y., Zhao, H., Xu, K., Ruan, L., and Wu, W. Deep Learning System to Screen Coronavirus Disease 2019 Pneumonia. arXiv preprint arXiv:2002.09334, 1-29, 2020.[15] Wang, S., Kang, B., Ma, J., Zeng, X., Xiao, M., Guo, J., Cai, M., Yang, J., Li, Y., Meng, X., and Xu, B. A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19). medRxiv preprint doi: https://doi.org/ 10.1101/2020.02.14.20023028, 1-26, 2020.[16] A.MERS-CoV Hamimi, Middle East respiratory syndrome corona virus: Can radiology be of help? Initial single center experience, The Egyptian Journal of Radiology and Nuclear Medicine 47 (1) (2016) 95–106.[17] X. Xie, X. Li, S. Wan, Y. Gong, Mining X-ray images of SARS patients, in: Graham J. Williams, Simeon J. Simoff (Eds.), Data Mining: Theory, Methodology, Techniques, and Applications, Springer-Verlag, Berlin, Heidelberg, 2006, pp. 282–294. ISBN: 3540325476.[18] Pennisi, M., Kavasidis, I., Spampinato, C., Schininà, V., Palazzo, S., Rundo, F., Cristofaro, M., Campioni, P., Pianura, E., Di Stefano, F. and Petrone, A., 2021. An Explainable AI System for Automated COVID-19 Assessment and Lesion Categorization from CT-scans. arXiv preprint arXiv:2101.11943.[19] Turkoglu, M., 2021. COVID-19 Detection System Using Chest CT Images and Multiple Kernels-Extreme Learning Machine Based on Deep Neural Network. IRBM.[20] M. Agarwal, L. Saba, S.K. Gupta, A. Carriero, Z. Falaschi, A. Paschè, P. Danna, A. El-Baz, S. Naidu, J.S. Suri, A Novel Block Imaging Technique Using Nine Artificial Intelligence Models for COVID-19 Disease Classification, Characterization and Severity Measurement in Lung Computed Tomography Scans on an Italian Cohort, Journal of Medical Systems 45 (3) (2021) 1–30.[21] M. Jamshidi, A. Lalbakhsh, J. Talla, Z. Peroutka, F. Hadjilooei, P. Lalbakhsh, M. Jamshidi, L. La Spada, M. Mirmozafari, M. Dehghani, A. Sabet, Artificial intelligence and COVID-19: deep learning approaches for diagnosis and treatment, IEEE Access 8 (2020) 109581–109595.[22] Hemdan, E.E.D., Shouman, M.A. and Karar, M.E., 2020. Covidx-net: A framework of deep learning classifiers to diagnose covid-19 in x-ray images. arXiv preprint arXiv:2003.11055.[23] S.J. Fong, G. Li, N. Dey, R.G. Crespo, E. Herrera-Viedma, Composite Monte Carlo decision making under high uncertainty of novel coronavirus epidemic using hybridized deep learning and fuzzy rule induction, Applied soft computing 93 (2020) 106282.[24] H. Louati, S. Bechikh, A. Louati, C.C. Hung, L.B. Said, Deep Convolutional Neural Network Architecture Design as a Bi-level Optimization Problem, Neurocomputing, 2021.[25] V. Ganesan, P. Rajarajeswari, V. Govindaraj, K.B. Prakash, J. Naren, Post– COVID-19 Emerging Challenges and Predictions on People, Process, and Product by Metaheuristic Deep Learning Algorithm, in: Machine Intelligence and Soft Computing, Springer, Singapore, 2021, pp. 275–287.[26] A. Jaiswal, N. Gianchandani, D. Singh, V. Kumar, M. Kaur, Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning, Journal of Biomolecular Structure and Dynamics (2020) 1–8.[27] M. Alazab, A. Awajan, A. Mesleh, A. Abraham, V. Jatana, S. 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Kikinis, Adaptive image filtering, in: Handbook of Medical Imaging Processing and Analysis, Academic press, 2000, pp. 3208–3212.[40] C Szegedy, S Iofe, V Vanhoucke, AA Alemi, Inception-v4, inception-resnet and the impact of residual connections on learning, in: In Thirty-First AAAI Conference on Artifcial Intelligence, Association for the Advancement of Artifcial Intelligence,USA, 2017, pp. 1–3.[41] M.Y. Sikkandar, B.A. Alrasheadi, N.B. Prakash, G.R. Hemalakshmi, A. Mohanarathinam, K. Shankar, Deep learning based an automated skin lesion segmentation and intelligent classification model, Journal of ambient intelligence and humanized computing (2020) 1–11.[42] COVID-19 Image Data Collection: Prospective Predictions Are the Future Joseph Paul Cohen and Paul Morrison and Lan Dao and Karsten Roth and Tim Q Duong and Marzyeh Ghassemi arXiv:2006.11988, https://github.com/ieee8023/covidchestxray-dataset, 2020PublicationORIGINALUnsupervised Deep Learning based Variational Autoencoder Model for COVID-19 Diagnosis and Classification.pdfUnsupervised Deep Learning based Variational Autoencoder Model for COVID-19 Diagnosis and Classification.pdfapplication/pdf53858https://repositorio.cuc.edu.co/bitstreams/806ab52d-79ed-45d8-9a5e-4335ab20c0c1/download2bf8b535b6fc7d6a1f6a54ee8b806205MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; 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Unsupervised deep learning based variational autoencoder model for COVID-19 diagnosis and classification

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