What question did this study set out to answer?

To assess how data-centric methods, such as image preprocessing and data augmentation, enhance the accuracy and reliability of pneumonia segmentation models on chest radiographs.

August 14, 2026The Egyptian Journal of Critical Care MedicineOpen Access

From data to diagnosis: evaluating the role of data-centric techniques in pneumonia segmentation with privacy considerations

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Authors

MSMehwish Shaikh QAQasim Ali Arain RJRabeea Jaffari

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Overview

Computational study demonstrates improved chest X-ray pneumonia segmentation using data-centric techniques, highlighting the value of enhanced preprocessing for privacy-aware diagnostic tools.

Key Points

To assess how data-centric methods, such as image preprocessing and data augmentation, enhance the accuracy and reliability of pneumonia segmentation models on chest radiographs.
Evaluated segmentation performance on a publicly available chest X-ray dataset under baseline conditions and with data-centric enhancements.
Assessed performance across multiple metrics, including accuracy, precision, recall, F1-score, Dice Similarity Coefficient (DSC), and Intersection over Union (IoU).
Structured preprocessing workflows to maintain compatibility with decentralized, privacy-preserving learning frameworks.
Data-centric enhancements increased segmentation accuracy from 69% to 85% and recall from 50% to 89%.
Overlap metrics showed marked improvements, with the Dice Similarity Coefficient rising from 62% to 80% and Intersection over Union increasing from 55% to 81%.

Cite This Study

Shaikh et al. (2026) studied this question.

synapsesocial.com/papers/6a7ec7bab70b84ec8b914512 https://doi.org/10.1007/s44349-026-00056-2

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August 14, 2026The Egyptian Journal of Critical Care MedicineOpen Access

From data to diagnosis: evaluating the role of data-centric techniques in pneumonia segmentation with privacy considerations

View Full Paper

Ask AI

Bookmark

Authors

MSMehwish Shaikh QAQasim Ali Arain RJRabeea Jaffari

Discussion

Member takes

Overview

Computational study demonstrates improved chest X-ray pneumonia segmentation using data-centric techniques, highlighting the value of enhanced preprocessing for privacy-aware diagnostic tools.

Key Points

To assess how data-centric methods, such as image preprocessing and data augmentation, enhance the accuracy and reliability of pneumonia segmentation models on chest radiographs.
Evaluated segmentation performance on a publicly available chest X-ray dataset under baseline conditions and with data-centric enhancements.
Assessed performance across multiple metrics, including accuracy, precision, recall, F1-score, Dice Similarity Coefficient (DSC), and Intersection over Union (IoU).
Structured preprocessing workflows to maintain compatibility with decentralized, privacy-preserving learning frameworks.
Data-centric enhancements increased segmentation accuracy from 69% to 85% and recall from 50% to 89%.
Overlap metrics showed marked improvements, with the Dice Similarity Coefficient rising from 62% to 80% and Intersection over Union increasing from 55% to 81%.

Cite This Study

Shaikh et al. (2026) studied this question.

synapsesocial.com/papers/6a7ec7bab70b84ec8b914512 https://doi.org/10.1007/s44349-026-00056-2

View Full Paper

Ask AI

Bookmark