ArticleIEEE access : practical innovations, open solutions2021
Bias Analysis on Public X-Ray Image Datasets of Pneumonia and COVID-19 Patients.
Article in IEEE access : practical innovations, open solutions, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 32 citations in OpenAlex.
- Identification of mathematical patterns in genomic spectrograms linked to variant classification in complete SARS-CoV-2 sequences.Scientific reports · 2025Article
- MIDAS: a technology-enabled hub-and-spoke system for the collection and dissemination of high-quality medical datasets in India.BMC medical informatics and decision making · 2025Article
- Deep Learning for Pneumonia Detection in Chest X-ray Images: A Comprehensive Survey.Journal of imaging · 2024Review
- Challenges issues and future recommendations of deep learning techniques for SARS-CoV-2 detection utilising X-ray and CT images: a comprehensive review.PeerJ. Computer science · 2024Article
- Digital Determinants of Health: Health data poverty amplifies existing health disparities-A scoping review.PLOS digital health · 2023Article
- Validating Automatic Concept-Based Explanations for AI-Based Digital Histopathology.Sensors (Basel, Switzerland) · 2022Article
- Explainable artificial intelligence-based edge fuzzy images for COVID-19 detection and identification.Applied soft computing · 2022Article
- Explainable Artificial Intelligence for Bias Detection in COVID CT-Scan Classifiers.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Chest X-ray images are useful for early COVID-19 diagnosis with the advantage that X-ray devices are already available in health centers and images are obtained immediately. Some datasets containing X-ray images with cases (pneumonia or COVID-19) and controls have been made available to develop machine-learning-based methods to aid in diagnosing the disease. However, these datasets are mainly composed of different sources coming from pre-COVID-19 datasets and COVID-19 datasets. Particularly, we have detected a significant bias in some of the released datasets used to train and test diagnostic systems, which might imply that the results published are optimistic and may overestimate the actual predictive capacity of the techniques proposed. In this article, we analyze the existing bias in some commonly used datasets and propose a series of preliminary steps to carry out before the classic machine learning pipeline in order to detect possible biases, to avoid them if possible and to report results that are more representative of the actual predictive power of the methods under analysis.
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