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COVID-19 Classification on Chest X-ray Images Using Deep Learning Methods

Medicine and Health

COVID-19 Classification on Chest X-ray Images Using Deep Learning Methods

P. B. Tchounwou, S. Zimeras, et al.

This groundbreaking study presents a comparison of five deep learning models for COVID-19 classification using chest X-ray images. Remarkably, ResNet101 outperformed the rest with an impressive precision, recall, and accuracy of 96%. This research was conducted by Paul B Tchounwou, Stelios Zimeras, Styliani Geronikolou, Marios Constantinou, Themis Exarchos, Aristidis G Vrahatis, and Panagiotis Vlamos.

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Playback language: English
Abstract
This study evaluates five deep learning models (ResNet50, ResNet101, DenseNet121, DenseNet169, and InceptionV3) for COVID-19 classification using chest X-ray images from the COVID-QU dataset. Transfer learning was employed, and the models were trained and validated on a large publicly available repository. ResNet101 achieved the best performance, reaching 96% precision, recall, and accuracy.
Publisher
International Journal of Environmental Research and Public Health
Published On
Jan 22, 2023
Authors
Paul B Tchounwou, Stelios Zimeras, Styliani Geronikolou, Marios Constantinou, Themis Exarchos, Aristidis G Vrahatis, Panagiotis Vlamos
Tags
COVID-19
deep learning
chest X-ray
transfer learning
ResNet
DenseNet
InceptionV3

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