ArticleScientific reports2021
ANFIS-Net for automatic detection of COVID-19.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Automatic diagnosis of schizophrenia and attention deficit hyperactivity disorder in rs-fMRI modality using convolutional autoencoder model and interval type-2 fuzzy regression.Cognitive neurodynamics · 2023Article
- RApid Throughput Screening for Asymptomatic COVID-19 Infection With an Electrocardiogram: A Prospective Observational Study.Mayo Clinic proceedings. Digital health · 2023Article
- iMIGS: An innovative AI based prediction system for selecting the best patient-specific glaucoma treatment.MethodsX · 2023Article
- BEMD-3DCNN-based method for COVID-19 detection.Computers in biology and medicine · 2022Article
- An alternative approach to determination of Covid-19 personal risk index by using fuzzy logic.Health and technology · 2022Article
- Wavelet packet and fuzzy logic theory for automatic fault detection in induction motor.Soft computing · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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.
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Registered trials
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