Cargando…
Cloud-based COVID-19 disease prediction system from X-Ray images using convolutional neural network on smartphone
COVID-19 has engulfed over 200 nations through human-to-human transmission, either directly or indirectly. Reverse Transcription-polymerase Chain Reaction (RT-PCR) has been endorsed as a standard COVID-19 diagnostic procedure but has caveats such as low sensitivity, the need for a skilled workforce,...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9684956/ https://www.ncbi.nlm.nih.gov/pubmed/36467434 http://dx.doi.org/10.1007/s11042-022-14232-w |
_version_ | 1784835400065351680 |
---|---|
author | Lanjewar, Madhusudan G. Shaikh, Arman Yusuf Parab, Jivan |
author_facet | Lanjewar, Madhusudan G. Shaikh, Arman Yusuf Parab, Jivan |
author_sort | Lanjewar, Madhusudan G. |
collection | PubMed |
description | COVID-19 has engulfed over 200 nations through human-to-human transmission, either directly or indirectly. Reverse Transcription-polymerase Chain Reaction (RT-PCR) has been endorsed as a standard COVID-19 diagnostic procedure but has caveats such as low sensitivity, the need for a skilled workforce, and is time-consuming. Coronaviruses show significant manifestation in Chest X-Ray (CX-Ray) images and, thus, can be a viable option for an alternate COVID-19 diagnostic strategy. An automatic COVID-19 detection system can be developed to detect the disease, thus reducing strain on the healthcare system. This paper discusses a real-time Convolutional Neural Network (CNN) based system for COVID-19 illness prediction from CX-Ray images on the cloud. The implemented CNN model displays exemplary results, with training accuracy being 99.94% and validation accuracy reaching 98.81%. The confusion matrix was utilized to assess the models’ outcome and achieved 99% precision, 98% recall, 99% F1 score, 100% training area under the curve (AUC) and 98.3% validation AUC. The same CX-Ray dataset was also employed to predict the COVID-19 disease with deep Convolution Neural Networks (DCNN), such as ResNet50, VGG19, InceptonV3, and Xception. The prediction outcome demonstrated that the present CNN was more capable than the DCNN models. The efficient CNN model was deployed to the Platform as a Service (PaaS) cloud. |
format | Online Article Text |
id | pubmed-9684956 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-96849562022-11-28 Cloud-based COVID-19 disease prediction system from X-Ray images using convolutional neural network on smartphone Lanjewar, Madhusudan G. Shaikh, Arman Yusuf Parab, Jivan Multimed Tools Appl Track 2: Medical Applications of Multimedia COVID-19 has engulfed over 200 nations through human-to-human transmission, either directly or indirectly. Reverse Transcription-polymerase Chain Reaction (RT-PCR) has been endorsed as a standard COVID-19 diagnostic procedure but has caveats such as low sensitivity, the need for a skilled workforce, and is time-consuming. Coronaviruses show significant manifestation in Chest X-Ray (CX-Ray) images and, thus, can be a viable option for an alternate COVID-19 diagnostic strategy. An automatic COVID-19 detection system can be developed to detect the disease, thus reducing strain on the healthcare system. This paper discusses a real-time Convolutional Neural Network (CNN) based system for COVID-19 illness prediction from CX-Ray images on the cloud. The implemented CNN model displays exemplary results, with training accuracy being 99.94% and validation accuracy reaching 98.81%. The confusion matrix was utilized to assess the models’ outcome and achieved 99% precision, 98% recall, 99% F1 score, 100% training area under the curve (AUC) and 98.3% validation AUC. The same CX-Ray dataset was also employed to predict the COVID-19 disease with deep Convolution Neural Networks (DCNN), such as ResNet50, VGG19, InceptonV3, and Xception. The prediction outcome demonstrated that the present CNN was more capable than the DCNN models. The efficient CNN model was deployed to the Platform as a Service (PaaS) cloud. Springer US 2022-11-24 /pmc/articles/PMC9684956/ /pubmed/36467434 http://dx.doi.org/10.1007/s11042-022-14232-w Text en © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Track 2: Medical Applications of Multimedia Lanjewar, Madhusudan G. Shaikh, Arman Yusuf Parab, Jivan Cloud-based COVID-19 disease prediction system from X-Ray images using convolutional neural network on smartphone |
title | Cloud-based COVID-19 disease prediction system from X-Ray images using convolutional neural network on smartphone |
title_full | Cloud-based COVID-19 disease prediction system from X-Ray images using convolutional neural network on smartphone |
title_fullStr | Cloud-based COVID-19 disease prediction system from X-Ray images using convolutional neural network on smartphone |
title_full_unstemmed | Cloud-based COVID-19 disease prediction system from X-Ray images using convolutional neural network on smartphone |
title_short | Cloud-based COVID-19 disease prediction system from X-Ray images using convolutional neural network on smartphone |
title_sort | cloud-based covid-19 disease prediction system from x-ray images using convolutional neural network on smartphone |
topic | Track 2: Medical Applications of Multimedia |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9684956/ https://www.ncbi.nlm.nih.gov/pubmed/36467434 http://dx.doi.org/10.1007/s11042-022-14232-w |
work_keys_str_mv | AT lanjewarmadhusudang cloudbasedcovid19diseasepredictionsystemfromxrayimagesusingconvolutionalneuralnetworkonsmartphone AT shaikharmanyusuf cloudbasedcovid19diseasepredictionsystemfromxrayimagesusingconvolutionalneuralnetworkonsmartphone AT parabjivan cloudbasedcovid19diseasepredictionsystemfromxrayimagesusingconvolutionalneuralnetworkonsmartphone |