A deep learning feature extraction-based hybrid approach for detecting pediatric pneumonia in chest X-ray images

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dc.contributor.authorBal U.
dc.contributor.authorBal A.
dc.contributor.authorMoral Ö.T.
dc.contributor.authorDüzgün F.
dc.contributor.authorGürbüz N.
dc.date.accessioned2024-07-22T08:01:38Z
dc.date.available2024-07-22T08:01:38Z
dc.date.issued2024
dc.description.abstractPneumonia is a disease caused by bacteria, viruses, and fungi that settle in the alveolar sacs of the lungs and can lead to serious health complications in humans. Early detection of pneumonia is necessary for early treatment to manage and cure the disease. Recently, machine learning-based pneumonia detection methods have focused on pneumonia in adults. Machine learning relies on manual feature engineering, whereas deep learning can automatically detect and extract features from data. This study proposes a deep learning feature extraction-based hybrid approach that combines deep learning and machine learning to detect pediatric pneumonia, which is difficult to standardize. The proposed hybrid approach enhances the accuracy of detecting pediatric pneumonia and simplifies the approach by eliminating the requirement for advanced feature extraction. The experiments indicate that the hybrid approach using a Medium Neural Network based on AlexNet feature extraction achieved a 97.9% accuracy rate and 98.0% sensitivity rate. The results show that the proposed approach achieved higher accuracy rates than state-of-the-art approaches. © Australasian College of Physical Scientists and Engineers in Medicine 2023.
dc.identifier.DOI-ID10.1007/s13246-023-01347-z
dc.identifier.issn26624729
dc.identifier.urihttp://akademikarsiv.cbu.edu.tr:4000/handle/123456789/11531
dc.language.isoEnglish
dc.publisherSpringer Science and Business Media Deutschland GmbH
dc.subjectChild
dc.subjectCOVID-19
dc.subjectDeep Learning
dc.subjectHumans
dc.subjectLung
dc.subjectPneumonia
dc.subjectX-Rays
dc.subjectDeep learning
dc.subjectExtraction
dc.subjectLearning systems
dc.subjectPediatrics
dc.subjectViruses
dc.subjectAccuracy rate
dc.subjectChest X-ray image
dc.subjectDeep learning
dc.subjectDetection methods
dc.subjectFeature engineerings
dc.subjectFeatures extraction
dc.subjectHealth complications
dc.subjectHybrid approach
dc.subjectMachine-learning
dc.subjectPediatric pneumonia detection
dc.subjectadolescent
dc.subjectadult
dc.subjectArticle
dc.subjectchild
dc.subjectclassifier
dc.subjectclinical article
dc.subjectcontrolled study
dc.subjectconvolutional neural network
dc.subjectdeep learning
dc.subjectdiagnostic accuracy
dc.subjectF1 score
dc.subjectfeature extraction
dc.subjectfemale
dc.subjectfollow up
dc.subjecthuman
dc.subjectimage processing
dc.subjectinfant
dc.subjectmale
dc.subjectmorbidity
dc.subjectmortality
dc.subjectnewborn
dc.subjectpneumonia
dc.subjectpreschool child
dc.subjectschool child
dc.subjectscoring system
dc.subjectstandardization
dc.subjectthorax radiography
dc.subjectyoung adult
dc.subjectcoronavirus disease 2019
dc.subjectdiagnostic imaging
dc.subjectlung
dc.subjectpneumonia
dc.subjectX ray
dc.subjectFeature extraction
dc.titleA deep learning feature extraction-based hybrid approach for detecting pediatric pneumonia in chest X-ray images
dc.typeArticle

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