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September 5, 2025Baghdad Science JournalOpen Access

PSACOVID: Parallel Segmentation Architecture for Localization and Contagion Quantification of Covid-19 Pneumonia in Chest Computed Tomography Imaging

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Authors

PHPayman Hussein HussanUniversity of BabylonIAIsraa Hadi AliUniversity of Babylon

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Implication

Artificial intelligence improves localization and quantification accuracy in lung CT scans, suggesting enhanced diagnostic capabilities for COVID-19.

Key Points

  • The framework achieves a sensitivity of 98.80% for accurate covid-19 detection.
  • Utilizing a public dataset of 20 patients, the system localizes and quantifies covid-19 pneumonia lesions.
  • An artificial intelligence model enhances diagnostic performance by improving anomaly detection in lung imaging.
  • Data augmentation techniques are applied to optimize the analysis of computed tomography images.

Cite This Study

Hussan et al. (2025) studied this question.

synapsesocial.com/papers/68bb3d4e2b87ece8dc955b06https://doi.org/10.21123/2411-7986.5037
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Also Consider

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  1. 1Segmentation and measurement of lung pathological changes for COVID-19 diagnosis based on computed tomography2021
  2. 2Augmented Artificial Intelligence Based Diagnosis of COVID‐19 With Synthesised Computed Tomography Images2026
  3. 3COVID Pneumonia Severity Detection of Chest CT-Scan Images based on Robust Semantic Segmentation2024 · 1 citations
  4. 4COVID-19 Lungs CT Scan Lesion Segmentation2024 · 2 citations
  5. 5Lung Infection Detection via CT Images and Transfer Learning Techniques in Deep Learning2024 · 2 citations