Medicine and HealthInternational Journal of Environmental Research and Public 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.
Related Publications
Explore these studies to deepen your understanding
Adjacent work that informs or extends this paper's methodology and findings.
Medicine and Health
COVID-19 Prognosis from Chest X-ray Images by using Deep Learning Approaches: A Next Generation Diagnostic Tool
M. Pal, S. Parij, et al.
Engineering and Technology
Automated classification of big X-ray diffraction data using deep learning models
J. E. Salgado, S. Lerman, et al.
Medicine and Health
Design and Analysis of a Deep Learning Ensemble Framework Model for the Detection of COVID-19 and Pneumonia Using Large-Scale CT Scan and X-ray Image Datasets
X. Xue, S. Chinnaperumal, et al.
Medicine and Health
CovidCTNet: an open-source deep learning approach to diagnose covid-19 using small cohort of CT images
T. Javaheri, M. Homayounfar, et al.
