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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Bibliographic Details
Published in:Pattern recognition letters Vol. 151; pp. 267 - 274
Main Authors: Mansour, Romany F., Escorcia-Gutierrez, José, Gamarra, Margarita, Gupta, Deepak, Castillo, Oscar, Kumar, Sachin
Format: Journal Article
Language:English
Published: Netherlands Elsevier B.V 01.11.2021
Elsevier Science Ltd
Subjects:
Artificial intelligence
Classification
Computer vision
Coronaviruses
COVID-19
Deep learning
Experimentation
Feature extraction
Image classification
Image enhancement
Image quality
Learning algorithms
Machine learning
Unsupervised learning
Variational autoencoder
Wiener filtering
COVID-19
Deep learning
Variational autoencoder
Unsupervised learning
Image classification
ISSN:0167-8655, 1872-7344, 0167-8655
Online Access:Get full text
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Summary:•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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ISSN:0167-8655
1872-7344
0167-8655
DOI:10.1016/j.patrec.2021.08.018