ANFIS-Net for automatic detection of COVID-19

Abstract Among the most leading causes of mortality across the globe are infectious diseases which have cost tremendous lives with the latest being coronavirus (COVID-19) that has become the most recent challenging issue. The extreme nature of this infectious virus and its ability to spread without...

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Autores principales:	Afnan Al-ali, Omar Elharrouss, Uvais Qidwai, Somaya Al-Maaddeed
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/7595cc9edd0f41a5af78727b6896fb18
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spelling	oai:doaj.org-article:7595cc9edd0f41a5af78727b6896fb182021-12-02T15:09:07ZANFIS-Net for automatic detection of COVID-1910.1038/s41598-021-96601-32045-2322https://doaj.org/article/7595cc9edd0f41a5af78727b6896fb182021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-96601-3https://doaj.org/toc/2045-2322Abstract Among the most leading causes of mortality across the globe are infectious diseases which have cost tremendous lives with the latest being coronavirus (COVID-19) that has become the most recent challenging issue. The extreme nature of this infectious virus and its ability to spread without control has made it mandatory to find an efficient auto-diagnosis system to assist the people who work in touch with the patients. As fuzzy logic is considered a powerful technique for modeling vagueness in medical practice, an Adaptive Neuro-Fuzzy Inference System (ANFIS) was proposed in this paper as a key rule for automatic COVID-19 detection from chest X-ray images based on the characteristics derived by texture analysis using gray level co-occurrence matrix (GLCM) technique. Unlike the proposed method, especially deep learning-based approaches, the proposed ANFIS-based method can work on small datasets. The results were promising performance accuracy, and compared with the other state-of-the-art techniques, the proposed method gives the same performance as the deep learning with complex architectures using many backbone.Afnan Al-aliOmar ElharroussUvais QidwaiSomaya Al-MaaddeedNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Afnan Al-ali Omar Elharrouss Uvais Qidwai Somaya Al-Maaddeed ANFIS-Net for automatic detection of COVID-19
description	Abstract Among the most leading causes of mortality across the globe are infectious diseases which have cost tremendous lives with the latest being coronavirus (COVID-19) that has become the most recent challenging issue. The extreme nature of this infectious virus and its ability to spread without control has made it mandatory to find an efficient auto-diagnosis system to assist the people who work in touch with the patients. As fuzzy logic is considered a powerful technique for modeling vagueness in medical practice, an Adaptive Neuro-Fuzzy Inference System (ANFIS) was proposed in this paper as a key rule for automatic COVID-19 detection from chest X-ray images based on the characteristics derived by texture analysis using gray level co-occurrence matrix (GLCM) technique. Unlike the proposed method, especially deep learning-based approaches, the proposed ANFIS-based method can work on small datasets. The results were promising performance accuracy, and compared with the other state-of-the-art techniques, the proposed method gives the same performance as the deep learning with complex architectures using many backbone.
format	article
author	Afnan Al-ali Omar Elharrouss Uvais Qidwai Somaya Al-Maaddeed
author_facet	Afnan Al-ali Omar Elharrouss Uvais Qidwai Somaya Al-Maaddeed
author_sort	Afnan Al-ali
title	ANFIS-Net for automatic detection of COVID-19
title_short	ANFIS-Net for automatic detection of COVID-19
title_full	ANFIS-Net for automatic detection of COVID-19
title_fullStr	ANFIS-Net for automatic detection of COVID-19
title_full_unstemmed	ANFIS-Net for automatic detection of COVID-19
title_sort	anfis-net for automatic detection of covid-19
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/7595cc9edd0f41a5af78727b6896fb18
work_keys_str_mv	AT afnanalali anfisnetforautomaticdetectionofcovid19 AT omarelharrouss anfisnetforautomaticdetectionofcovid19 AT uvaisqidwai anfisnetforautomaticdetectionofcovid19 AT somayaalmaaddeed anfisnetforautomaticdetectionofcovid19
_version_	1718387881395879936

ANFIS-Net for automatic detection of COVID-19

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