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AuthorAl-ali A.
AuthorElharrouss O.
AuthorQidwai U.
AuthorAl-Maadeed, Somaya
Available date2022-05-19T10:23:06Z
Publication Date2021
Publication NameScientific Reports
ResourceScopus
Identifierhttp://dx.doi.org/10.1038/s41598-021-96601-3
URIhttp://hdl.handle.net/10576/31082
AbstractAmong 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.
SponsorThis publication was made by NPRP Grant NPRP12S-0312-190332 from the Qatar National Research Fund (a member of the Qatar Foundation). The statements made herein are solely the responsibility of the authors.
Languageen
PublisherNature Research
Subjectalgorithm
comparative study
computer assisted diagnosis
diagnostic imaging
early diagnosis
fuzzy logic
human
procedures
radiography
Algorithms
COVID-19
Deep Learning
Early Diagnosis
Fuzzy Logic
Humans
Radiographic Image Interpretation, Computer-Assisted
Radiography
TitleANFIS-Net for automatic detection of COVID-19
TypeArticle
Issue Number1
Volume Number11
dc.accessType Abstract Only


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