FACNN: fuzzy-based adaptive convolution neural network for classifying COVID-19 in noisy CXR images

COVID-19 detection using chest X-rays (CXR) has evolved as a significant method for early diagnosis of the pandemic disease. Clinical trials and methods utilize X-ray images with computer and intelligent algorithms to improve detection and classification precision. This article thus proposes a fuzzy...

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Bibliographic Details
Published in:Medical & biological engineering & computing 2024-09, Vol.62 (9), p.2893-2909
Main Authors: S, Suganyadevi, V, Seethalakshmi
Format: Article
Language:English
Subjects:
Adaptive algorithms
Algorithms
Artificial neural networks
Availability
Biomedical and Life Sciences
Biomedical Engineering and Bioengineering
Biomedicine
Classification
Clinical trials
Computer Applications
COVID-19
COVID-19 - diagnostic imaging
Feature extraction
Fuzzy Logic
Human Physiology
Humans
Imaging
Medical imaging
Neural networks
Neural Networks, Computer
Original Article
Radiography, Thoracic - methods
Radiology
SARS-CoV-2
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Summary:COVID-19 detection using chest X-rays (CXR) has evolved as a significant method for early diagnosis of the pandemic disease. Clinical trials and methods utilize X-ray images with computer and intelligent algorithms to improve detection and classification precision. This article thus proposes a fuzzy-based adaptive convolution neural network (FACNN) model to improve the detection precision by confining the false rates. The feature extraction process between the successive regions is validated using a fuzzy process that classifies labeled and unknown pixels. The membership functions are derived based on high precision features for detection and false rate suppression process. The convolution neural network process is responsible for increasing detection precision through recurrent training based on feature availability. This availability analysis is verified using fuzzy derivatives under local variances. Based on variance-reduced features, the appropriate regions with labeled and unknown features are used for normal or infected classification. Thus, the proposed FACNN improves accuracy, precision, and feature extraction by 14.36%, 8.74%, and 12.35%, respectively. This model reduces the false rate and extraction time by 10.35% and 10.66%, respectively. Graphical Abstract Proposed FACNN Model
ISSN:0140-0118
1741-0444
1741-0444
DOI:10.1007/s11517-024-03107-x