Adaptive Fuzzy Neural Network vs. Convolution Neural Network in Classifying COVID-19 from Chest X-rays
2022 IEEE GLOBECOM Workshops, GC Wkshps 2022
; : 1080-1083, 2022.
Article
in English
| Scopus | ID: covidwho-2227398
ABSTRACT
Detecting COVID-19 in the early time can save lives and reduce the cost of huge pressure on healthcare centers. Many machine and deep learning models have been proposed by researchers to detect and diagnose COVID-19 based on chest X-rays. However, we need to know which of those models is more effective and efficient. This paper presents a comparative study between adaptive fuzzy neural network (AFNN) and convolutional neural network (CNN) in classifying COVID-19 using chest X-rays. We present the experimental results showing the comparative performance measures with respect to the size of available dataset. We also present the relative advantage of each family of neural network in accuracy, precision, recall, F1score, and the computation time. © 2022 IEEE.
Adaptive fuzzy neural network; convolutional neural network; machine learning for healthcare; Convolution; Convolutional neural networks; Deep learning; Fuzzy inference; Fuzzy neural networks; Health care; Adaptive-fuzzy-neural-network; Comparative performance; Comparatives studies; Convolution neural network; Learning models; Machine-learning; Neural-networks; Performance measure; COVID-19
Full text:
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Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
2022 IEEE GLOBECOM Workshops, GC Wkshps 2022
Year:
2022
Document Type:
Article
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