RNGU-NET: a novel efficient approach in Segmenting Tuberculosis using chest X-Ray images

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dc.contributor.authorTurk, Fuat
dc.date.accessioned2025-01-21T16:44:17Z
dc.date.available2025-01-21T16:44:17Z
dc.date.issued2024
dc.departmentKırıkkale Üniversitesi
dc.description.abstractTuberculosis affects various tissues, including the lungs, kidneys, and brain. According to the medical report published by the World Health Organization (WHO) in 2020, approximately ten million people have been infected with tuberculosis. U-NET, a preferred method for detecting tuberculosis-like cases, is a convolutional neural network developed for segmentation in biomedical image processing. The proposed RNGU-NET architecture is a new segmentation technique combining the ResNet, Non-Local Block, and Gate Attention Block architectures. In the RNGU-NET design, the encoder phase is strengthened with ResNet, and the decoder phase incorporates the Gate Attention Block. The key innovation lies in the proposed Local Non-Local Block architecture, overcoming the bottleneck issue in U-Net models. In this study, the effectiveness of the proposed model in tuberculosis segmentation is compared to the U-NET, U-NET+ResNet, and RNGU-NET algorithms using the Shenzhen dataset. According to the results, the RNGU-NET architecture achieves the highest accuracy rate of 98.56%, Dice coefficient of 97.21%, and Jaccard index of 96.87% in tuberculosis segmentation. Conversely, the U-NET model exhibits the lowest accuracy and Jaccard index scores, while U-NET+ResNet has the poorest Dice coefficient. These findings underscore the success of the proposed RNGU-NET method in tuberculosis segmentation.
dc.identifier.doi10.7717/peerj-cs.1780
dc.identifier.issn2376-5992
dc.identifier.pmid38435571
dc.identifier.scopus2-s2.0-85185828447
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.7717/peerj-cs.1780
dc.identifier.urihttps://hdl.handle.net/20.500.12587/25412
dc.identifier.volume10
dc.identifier.wosWOS:001336013300001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherPeerj Inc
dc.relation.ispartofPeerj Computer Science
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241229
dc.subjectMedical image segmentation; Tuberculosis segmentation; Non-Local Block; RNGU-Net model
dc.titleRNGU-NET: a novel efficient approach in Segmenting Tuberculosis using chest X-Ray images
dc.typeArticle

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