Ensemble Deep Learning for the Detection of COVID-19 in Unbalanced Chest X-ray Dataset
The ongoing COVID-19 pandemic has caused devastating effects on humanity worldwide. With practical advantages and wide accessibility, chest X-rays (CXRs) play vital roles in the diagnosis of COVID-19 and the evaluation of the extent of lung damages incurred by the virus. This study aimed to leverage...
Main Authors: | Khin Yadanar Win, Noppadol Maneerat, Syna Sreng, Kazuhiko Hamamoto |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2021-11-01
|
Series: | Applied Sciences |
Subjects: | |
Online Access: | https://www.mdpi.com/2076-3417/11/22/10528 |
Similar Items
-
Deep Learning for Optic Disc Segmentation and Glaucoma Diagnosis on Retinal Images
by: Syna Sreng, et al.
Published: (2020-07-01) -
Automated Diabetic Retinopathy Screening System Using Hybrid Simulated Annealing and Ensemble Bagging Classifier
by: Syna Sreng, et al.
Published: (2018-07-01) -
Deep Ensemble Learning for the Automatic Detection of Pneumoconiosis in Coal Worker’s Chest X-ray Radiography
by: Liton Devnath, et al.
Published: (2022-09-01) -
A Hybrid VDV Model for Automatic Diagnosis of Pneumothorax Using Class-Imbalanced Chest X-Rays Dataset
by: Tahira Iqbal, et al.
Published: (2022-01-01) -
Integrating oversampling and ensemble-based machine learning techniques for an imbalanced dataset in dyslexia screening tests
by: Shahriar Kaisar, et al.
Published: (2022-12-01)