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AI system uses geometric normalization for accurate pneumonia and COVID-19 detection in X-rays

By PulseAugur Editorial · [1 sources] ·

Researchers have developed an automated system for identifying pneumonia and COVID-19 in chest X-rays by geometrically normalizing the lung region. The system employs a ResNet-18 model for landmark detection, followed by a geometric normalization process using Generalized Procrustes Analysis and affine warping. A separate ResNet-18 classifier then categorizes images as COVID-19, viral pneumonia, or normal. This approach achieved high accuracy on the COVID-19 Radiography Database, demonstrating that anatomical alignment can yield more robust and artifact-resistant disease recognition compared to raw or artifact-masked images. AI

IMPACT This research demonstrates a novel application of AI in medical imaging, potentially improving diagnostic accuracy and artifact resistance for pulmonary disease detection.

RANK_REASON The cluster contains a research paper detailing a novel method for medical image analysis. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AI system uses geometric normalization for accurate pneumonia and COVID-19 detection in X-rays

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The cluster contains a research paper detailing a novel method for medical image analysis. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Salvador E. Ayala-Raggi, Rafael Alejandro Cruz-Ovando, Lauro Reyes-Cocoletzi, Aldrin Barreto-Flores ·

    Accurate Recognition of Pneumonia and COVID-19 by Geometric Shape Normalization of Lung Region using Automatic Landmark Detection and Piecewise Affine Warping

    arXiv:2606.29715v1 Announce Type: new Abstract: This paper presents an automatic system for recognizing pulmonary diseases in chest X-rays using geometric normalization of the lung region. The method combines three modules: (1) a ResNet-18 landmark detector with coordinate attent…

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