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المؤلفAl-ali A.
المؤلفElharrouss O.
المؤلفQidwai U.
المؤلفAl-Maadeed, Somaya
تاريخ الإتاحة2022-05-19T10:23:06Z
تاريخ النشر2021
اسم المنشورScientific Reports
المصدرScopus
المعرّفhttp://dx.doi.org/10.1038/s41598-021-96601-3
معرّف المصادر الموحدhttp://hdl.handle.net/10576/31082
الملخصAmong 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.
راعي المشروعThis 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.
اللغةen
الناشرNature Research
الموضوعalgorithm
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
العنوانANFIS-Net for automatic detection of COVID-19
النوعArticle
رقم العدد1
رقم المجلد11
dc.accessType Abstract Only


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