mlsanityData to 7 Oct 2026

Automated detection of COVID-19 cases from chest X-ray images using deep neural network and XGBoost

arXiv 2109.02428 ↗First posted 3 Sep 2021eess.IV cs.CV cs.LGcited by 8

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Abstract (arXiv)

In late 2019 and after COVID-19 pandemic in the world, many researchers and scholars have tried to provide methods for detection of COVID-19 cases. Accordingly, this study focused on identifying COVID-19 cases from chest X-ray images. In this paper, a novel approach to diagnosing coronavirus disease from X-ray images was proposed. In the proposed method, DenseNet169 deep neural network was used to extract the features of X-ray images taken from the patients' chest and the extracted features were then given as input to the Extreme Gradient Boosting (XGBoost) algorithm so that it could perform the classification task. Evaluation of the proposed approach and its comparison with the methods presented in recent years revealed that the proposed method was more accurate and faster than the existing ones and had an acceptable performance in detection of COVID-19 cases from X-ray images.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 10 of its 63 references that are in the map. First posted on arXiv 3 Sep 2021.

Builds on

10 of 63 references.

References · 10

  1. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,690
  2. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,120
  3. 2017ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseasescited by 969
  4. 2020COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Imagescited by 264
  5. 2020COVID-19 Image Data Collectioncited by 100
  6. 2020Covid-19: Automatic detection from X-Ray images utilizing Transfer Learning with Convolutional Neural Networkscited by 92
  7. 2020Automatic Detection of Coronavirus Disease (COVID-19) Using X-ray Images and Deep Convolutional Neural Networkscited by 86
  8. 2020COVIDX-Net: A Framework of Deep Learning Classifiers to Diagnose COVID-19 in X-Ray Imagescited by 60
  9. 2018Lung and Pancreatic Tumor Characterization in the Deep Learning Era: Novel Supervised and Unsupervised Learning Approachescited by 14
  10. 2020Deep Learning in Detection and Diagnosis of Covid-19 using Radiology Modalities: A Systematic Reviewcited by 6

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Cited by year

Citing papers per year: 2021: 1; 2022: 5; 2023: 2.

8 papers.

  1. 2021A novel framework based on deep learning and ANOVA feature selection method for diagnosis of COVID-19 cases from chest X-ray Imagescited by 6
  2. 2022Diagnosis of COVID-19 Cases from Chest X-ray Images Using Deep Neural Network and LightGBMcited by 4
  3. 2022Classification of COVID-19 in Chest X-ray Images Using Fusion of Deep Features and LightGBMcited by 3
  4. 2022Diagnosis of Parkinson's Disease Based on Voice Signals Using SHAP and Hard Voting Ensemble Methodcited by 3
  5. 2022Classification of Breast Tumours Based on Histopathology Images Using Deep Features and Ensemble of Gradient Boosting Methodscited by 2
  6. 2023PD-ADSV: An Automated Diagnosing System Using Voice Signals and Hard Voting Ensemble Method for Parkinson's Diseasecited by 1
  7. 2022CCTCOVID: COVID-19 Detection from Chest X-Ray Images Using Compact Convolutional Transformerscited by 0
  8. 2023SyMPox: An Automated Monkeypox Detection System Based on Symptoms Using XGBoostcited by 0