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

• 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
• ISSN: 0140-0118; 1741-0444; 1741-0444
• DOI: 10.1007/s11517-024-03107-x

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