Unsupervised Deep Learning based Variational Autoencoder Model for COVID-19 Diagnosis and Classification
•Design a novel Unsupervised DL model for COVID-19 Diagnosis and Classification•Propose a Parameter Tuned Adagrad with Inception v4 model as feature extractor•Employ an Unsupervised Variational Autoencoder Model for classification process•Validate the classification performance on COVID Chest X-ray...
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| Vydáno v: | Pattern recognition letters Ročník 151; s. 267 - 274 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Netherlands
Elsevier B.V
01.11.2021
Elsevier Science Ltd |
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| ISSN: | 0167-8655, 1872-7344, 0167-8655 |
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| Abstract | •Design a novel Unsupervised DL model for COVID-19 Diagnosis and Classification•Propose a Parameter Tuned Adagrad with Inception v4 model as feature extractor•Employ an Unsupervised Variational Autoencoder Model for classification process•Validate the classification performance on COVID Chest X-ray dataset
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. |
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| AbstractList | 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. •Design a novel Unsupervised DL model for COVID-19 Diagnosis and Classification•Propose a Parameter Tuned Adagrad with Inception v4 model as feature extractor•Employ an Unsupervised Variational Autoencoder Model for classification process•Validate the classification performance on COVID Chest X-ray dataset 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. 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.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. |
| Author | Castillo, Oscar Escorcia-Gutierrez, José Kumar, Sachin Gamarra, Margarita Mansour, Romany F. Gupta, Deepak |
| Author_xml | – sequence: 1 givenname: Romany F. surname: Mansour fullname: Mansour, Romany F. email: romanyf@scinv.au.edu.eg organization: Department of Mathematics, Faculty of Science, New Valley University, El-Kharga 72511, Egypt – sequence: 2 givenname: José surname: Escorcia-Gutierrez fullname: Escorcia-Gutierrez, José email: jose.escorcia23@gmail.com organization: Electronic and telecommunications program, Universidad Autónoma del Caribe, Barranquilla, Colombia – sequence: 3 givenname: Margarita surname: Gamarra fullname: Gamarra, Margarita email: mrgamarra22@gmail.com organization: Department of Computational Science and Electronic, Universidad de la Costa, CUC, Barranquilla, Colombia – sequence: 4 givenname: Deepak surname: Gupta fullname: Gupta, Deepak email: deepakgupta@mait.ac.in organization: Department of Computer Science & Engineering, Maharaja Agrasen Institute of Technology, Delhi, India – sequence: 5 givenname: Oscar surname: Castillo fullname: Castillo, Oscar email: ocastillo@tectijuana.mx organization: Tijuana Institute Technology, Tijuana, Mexico – sequence: 6 givenname: Sachin surname: Kumar fullname: Kumar, Sachin email: sachinagnihotri16@gmail.com organization: Department of Computer Science, South Ural State University, Chelyabinsk, Russian Federation |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/34566223$$D View this record in MEDLINE/PubMed |
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| Keywords | COVID-19 Deep learning Variational autoencoder Unsupervised learning Image classification |
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| License | 2021 Elsevier B.V. All rights reserved. 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 website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
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| Title | Unsupervised Deep Learning based Variational Autoencoder Model for COVID-19 Diagnosis and Classification |
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