mlsanityData to 14 Oct 2026

Classification of COVID-19 from CXR Images in a 15-class Scenario: an Attempt to Avoid Bias in the System

arXiv 2109.12453 ↗First posted 25 Sep 2021eess.IV cs.CV cs.LGcited by 1

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Abstract (arXiv)

As of June 2021, the World Health Organization (WHO) has reported 171.7 million confirmed cases including 3,698,621 deaths from COVID-19. Detecting COVID-19 and other lung diseases from Chest X-Ray (CXR) images can be very effective for emergency diagnosis and treatment as CXR is fast and cheap. The objective of this study is to develop a system capable of detecting COVID-19 along with 14 other lung diseases from CXRs in a fair and unbiased manner. The proposed system consists of a CXR image selection technique and a deep learning based model to classify 15 diseases including COVID-19. The proposed CXR selection technique aims to retain the maximum variation uniformly and eliminate poor quality CXRs with the goal of reducing the training dataset size without compromising classifier accuracy. More importantly, it reduces the often hidden bias and unfairness in decision making. The proposed solution exhibits a promising COVID-19 detection scheme in a more realistic situation than most existing studies as it deals with 15 lung diseases together. We hope the proposed method will have wider adoption in medical image classification and other related fields.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 35 of its 72 references that are in the map. First posted on arXiv 25 Sep 2021.

Builds on

35 of 72 references.

References · 35

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  3. 2016Densely Connected Convolutional Networkscited by 6,720
  4. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,475
  5. 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,845
  6. 2016Xception: Deep Learning with Depthwise Separable Convolutionscited by 2,332
  7. 2016Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learningcited by 1,512
  8. 2019CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparisoncited by 1,423
  9. 2017ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseasescited by 972
  10. 2017CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learningcited by 461
  11. 2020COVID-19 Image Data Collectioncited by 101
  12. 2020Covid-19: Automatic detection from X-Ray images utilizing Transfer Learning with Convolutional Neural Networkscited by 92
23 more
  1. 2020Automatic Detection of Coronavirus Disease (COVID-19) Using X-ray Images and Deep Convolutional Neural Networkscited by 86
  2. 2020CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray imagescited by 68
  3. 2020Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation and Diagnosis for COVID-19cited by 62
  4. 2018Comparison of Deep Learning Approaches for Multi-Label Chest X-Ray Classificationcited by 57
  5. 2020Estimating Uncertainty and Interpretability in Deep Learning for Coronavirus (COVID-19) Detectioncited by 46
  6. 2020Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural networkcited by 43
  7. 2018Diagnose like a Radiologist: Attention Guided Convolutional Neural Network for Thorax Disease Classificationcited by 36
  8. 2020COVID-CAPS: A Capsule Network-based Framework for Identification of COVID-19 cases from X-ray Imagescited by 34
  9. 2018Chest X-rays Classification: A Multi-Label and Fine-Grained Problemcited by 31
  10. 2020Iteratively Pruned Deep Learning Ensembles for COVID-19 Detection in Chest X-rayscited by 21
  11. 2020Unveiling COVID-19 from Chest X-ray with deep learning: a hurdles race with small datacited by 20
  12. 2020COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarioscited by 19
  13. 2020COVID-DA: Deep Domain Adaptation from Typical Pneumonia to COVID-19cited by 17
  14. 2020Finding Covid-19 from Chest X-rays using Deep Learning on a Small Datasetcited by 16
  15. 2020Towards an Effective and Efficient Deep Learning Model for COVID-19 Patterns Detection in X-ray Imagescited by 13
  16. 2019Chester: A Web Delivered Locally Computed Chest X-Ray Disease Prediction Systemcited by 12
  17. 2020DeepCOVIDExplainer: Explainable COVID-19 Diagnosis Based on Chest X-ray Imagescited by 11
  18. 2020A Deep Convolutional Neural Network for COVID-19 Detection Using Chest X-Rayscited by 11
  19. 2020CovidAID: COVID-19 Detection Using Chest X-Raycited by 7
  20. 2020A Cascaded Learning Strategy for Robust COVID-19 Pneumonia Chest X-Ray Screeningcited by 6
  21. 2020A modified deep convolutional neural network for detecting COVID-19 and pneumonia from chest X-ray images based on the concatenation of Xception and ResNet50V2cited by 4
  22. 2020COVID-MobileXpert: On-Device COVID-19 Patient Triage and Follow-up using Chest X-rayscited by 3
  23. 2020Deep Convolutional Neural Networks to Diagnose COVID-19 and other Pneumonia Diseases from Posteroanterior Chest X-Rayscited by 2

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  1. 2026Exponential Pixelating Integral transform with dual fractal features for enhanced chest X-ray abnormality detectioncited by 0