Lung Disease Detection Using U-Net Feature Extractor Cascaded by Graph Convolutional Network

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dc.contributor.authorRashid, Pshtiwan Qader
dc.contributor.authorTurker, Ilker
dc.date.accessioned2024-09-29T16:08:05Z
dc.date.available2024-09-29T16:08:05Z
dc.date.issued2024
dc.departmentKarabük Üniversitesien_US
dc.description.abstractComputed tomography (CT) scans have recently emerged as a major technique for the fast diagnosis of lung diseases via image classification techniques. In this study, we propose a method for the diagnosis of COVID-19 disease with improved accuracy by utilizing graph convolutional networks (GCN) at various layer formations and kernel sizes to extract features from CT scan images. We apply a U-Net model to aid in segmentation and feature extraction. In contrast with previous research retrieving deep features from convolutional filters and pooling layers, which fail to fully consider the spatial connectivity of the nodes, we employ GCNs for classification and prediction to capture spatial connectivity patterns, which provides a significant association benefit. We handle the extracted deep features to form an adjacency matrix that contains a graph structure and pass it to a GCN along with the original image graph and the largest kernel graph. We combine these graphs to form one block of the graph input and then pass it through a GCN with an additional dropout layer to avoid overfitting. Our findings show that the suggested framework, called the feature-extracted graph convolutional network (FGCN), performs better in identifying lung diseases compared to recently proposed deep learning architectures that are not based on graph representations. The proposed model also outperforms a variety of transfer learning models commonly used for medical diagnosis tasks, highlighting the abstraction potential of the graph representation over traditional methods.en_US
dc.identifier.doi10.3390/diagnostics14121313
dc.identifier.issn2075-4418
dc.identifier.issue12en_US
dc.identifier.pmid38928728en_US
dc.identifier.scopus2-s2.0-85197901633en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.3390/diagnostics14121313
dc.identifier.urihttps://hdl.handle.net/20.500.14619/7345
dc.identifier.volume14en_US
dc.identifier.wosWOS:001254606100001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherMdpien_US
dc.relation.ispartofDiagnosticsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectlung disease detectionen_US
dc.subjectgraph convolutional networksen_US
dc.subjectCOVID-19en_US
dc.subjectgraph representative learningen_US
dc.subjectdeep learningen_US
dc.titleLung Disease Detection Using U-Net Feature Extractor Cascaded by Graph Convolutional Networken_US
dc.typeArticleen_US

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