Bal U.Bal A.Moral Ö.T.Düzgün F.Gürbüz N.2024-07-222024-07-22202426624729http://akademikarsiv.cbu.edu.tr:4000/handle/123456789/11531Pneumonia 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.EnglishChildCOVID-19Deep LearningHumansLungPneumoniaX-RaysDeep learningExtractionLearning systemsPediatricsVirusesAccuracy rateChest X-ray imageDeep learningDetection methodsFeature engineeringsFeatures extractionHealth complicationsHybrid approachMachine-learningPediatric pneumonia detectionadolescentadultArticlechildclassifierclinical articlecontrolled studyconvolutional neural networkdeep learningdiagnostic accuracyF1 scorefeature extractionfemalefollow uphumanimage processinginfantmalemorbiditymortalitynewbornpneumoniapreschool childschool childscoring systemstandardizationthorax radiographyyoung adultcoronavirus disease 2019diagnostic imaginglungpneumoniaX rayFeature extractionArticle10.1007/s13246-023-01347-z