Automatic classification of pediatric pneumonia based on lung ultrasound pattern recognition

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Correa M.
Zimic M.
Barrientos F.
Barrientos R.
Román-Gonzalez A.
Pajuelo M.J.
Anticona C.
Mayta H.
Alva A.
Solis-Vasquez L.
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Public Library of Science
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Pneumonia is one of the major causes of child mortality, yet with a timely diagnosis, it is usually curable with antibiotic therapy. In many developing regions, diagnosing pneumonia remains a challenge, due to shortages of medical resources. Lung ultrasound has proved to be a useful tool to detect lung consolidation as evidence of pneumonia. However, diagnosis of pneumonia by ultrasound has limitations: it is operator-dependent, and it needs to be carried out and interpreted by trained personnel. Pattern recognition and image analysis is a potential tool to enable automatic diagnosis of pneumonia consolidation without requiring an expert analyst. This paper presents a method for automatic classification of pneumonia using ultrasound imaging of the lungs and pattern recognition.
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male, Article, artificial neural network, automation, child, clinical article, controlled study, digital imaging, disease classification, echography, female, human, image analysis, image processing, infant, lung infiltrate