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dc.contributor.authorShafi, Imran
dc.contributor.authorDin, Sadia
dc.contributor.authorKhan, Asim
dc.contributor.authorTorre Díez, Isabel de la 
dc.contributor.authorPali Casanova, Ramón del Jesús
dc.contributor.authorPifarre, Kilian Tutusaus
dc.contributor.authorAshraf, Imran
dc.date.accessioned2023-06-30T11:19:41Z
dc.date.available2023-06-30T11:19:41Z
dc.date.issued2022
dc.identifier.citationCancers, 2022, Vol. 4, Nº. 21, 5457es
dc.identifier.issn2072-6694es
dc.identifier.urihttps://uvadoc.uva.es/handle/10324/60020
dc.descriptionProducción Científicaes
dc.description.abstractThe diagnosis of early-stage lung cancer is challenging due to its asymptomatic nature, especially given the repeated radiation exposure and high cost of computed tomography(CT). Examining the lung CT images to detect pulmonary nodules, especially the cell lung cancer lesions, is also tedious and prone to errors even by a specialist. This study proposes a cancer diagnostic model based on a deep learning-enabled support vector machine (SVM). The proposed computer-aided design (CAD) model identifies the physiological and pathological changes in the soft tissues of the cross-section in lung cancer lesions. The model is first trained to recognize lung cancer by measuring and comparing the selected profile values in CT images obtained from patients and control patients at their diagnosis. Then, the model is tested and validated using the CT scans of both patients and control patients that are not shown in the training phase. The study investigates 888 annotated CT scans from the publicly available LIDC/IDRI database. The proposed deep learning-assisted SVM-based model yields 94% accuracy for pulmonary nodule detection representing early-stage lung cancer. It is found superior to other existing methods including complex deep learning, simple machine learning, and the hybrid techniques used on lung CT images for nodule detection. Experimental results demonstrate that the proposed approach can greatly assist radiologists in detecting early lung cancer and facilitating the timely management of patients.es
dc.format.mimetypeapplication/pdfes
dc.language.isoenges
dc.publisherMDPIes
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectLungs - Canceres
dc.subjectLungs - Diseases - Diagnosises
dc.subjectLungs - Cancer - Treatmentes
dc.subjectCancer - Diagnosises
dc.subjectPulmones - Cánceres
dc.subjectPulmones - Cáncer - Diagnósticoes
dc.subjectRadiologyes
dc.subjectTomografía computadaes
dc.subjectPulmones - Radiografiaes
dc.subjectOncologyes
dc.titleAn effective method for lung cancer diagnosis from CT scan using deep learning-based support vector networkes
dc.typeinfo:eu-repo/semantics/articlees
dc.rights.holder© 2022 The Authorses
dc.identifier.doi10.3390/cancers14215457es
dc.relation.publisherversionhttps://www.mdpi.com/2072-6694/14/21/5457es
dc.identifier.publicationfirstpage5457es
dc.identifier.publicationissue21es
dc.identifier.publicationtitleCancerses
dc.identifier.publicationvolume14es
dc.peerreviewedSIes
dc.identifier.essn2072-6694es
dc.rightsAtribución 4.0 Internacional*
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones
dc.subject.unesco3201.01 Oncologíaes


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