Detection of Pneumonia Using A Hybrid Approach Consisting of MobileNetV2 and Squeeze-and-Excitation Network

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dc.contributor.authorFırat, Hüseyin
dc.contributor.authorÜzen, Hüseyin
dc.date.accessioned2025-02-22T14:13:30Z
dc.date.available2025-02-22T14:13:30Z
dc.date.issued2024
dc.departmentDicle Üniversitesien_US
dc.description.abstractPneumonia is a global health concern, responsible for a significant number of deaths. Its diagnostic challenge arises from visual similarities it shares with various respiratory diseases, such as tuberculosis, complicating accurate identification. Furthermore, the variability in acquiring and processing chest X-ray (CXR) images can impact image quality, posing a hurdle for dependable algorithm development. To address this, resilient data-centric algorithms, trained on comprehensive datasets and validated through diverse imaging methods and radiology expertise, are imperative. This study presents a deep learning approach designed to distinguish between normal and pneumonia cases. The model, a hybrid of MobileNetV2 and the Squeeze-and-Excitation (SE) block, aims to reduce learnable parameters while enhancing feature extraction and classification. Integration of the SE block enhances classification performance, despite a slight parameter increase. The model was trained and tested on a dataset of 5856 CXR images from Kaggle's medical imaging challenge. Results demonstrated the model's exceptional performance, achieving an accuracy of 98.81%, precision of 98.79%, recall rate of 98.24%, and F1-score of 98.51%. Comparative analysis with various Convolutional neural network-based pre-trained models and recent literature studies confirmed its superiority, solidifying its potential as a robust tool for pneumonia detection, thus addressing a critical healthcare need.en_US
dc.identifier.doi10.46810/tdfd.1363218
dc.identifier.endpage61en_US
dc.identifier.issn2149-6366
dc.identifier.issue1en_US
dc.identifier.startpage54en_US
dc.identifier.trdizinid1230542en_US
dc.identifier.urihttps://doi.org/10.46810/tdfd.1363218
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/1230542
dc.identifier.urihttps://hdl.handle.net/11468/30026
dc.identifier.volume13en_US
dc.indekslendigikaynakTR-Dizin
dc.language.isoenen_US
dc.relation.ispartofTürk Doğa ve Fen Dergisien_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmzKA_TR_20250222
dc.subjectDeep learningen_US
dc.subjectMobileNetV2en_US
dc.subjectChest X-Ray imagesen_US
dc.subjectPneumonia detectionen_US
dc.subjectSqueeze-and-Excitation Networken_US
dc.titleDetection of Pneumonia Using A Hybrid Approach Consisting of MobileNetV2 and Squeeze-and-Excitation Networken_US
dc.typeArticleen_US

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