Detection of ring cell cancer in histopathological images with region of interest determined by SLIC superpixels method

Basit Öğe Kaydı
dc.authorid0000-0002-8470-4579en_US
dc.authorid0000-0002-3769-0071en_US
dc.contributor.authorBudak, Cafer
dc.contributor.authorMençik, Vasfiye
dc.date.accessioned2023-03-24T11:59:34Z
dc.date.available2023-03-24T11:59:34Z
dc.date.issued2022en_US
dc.departmentDicle Üniversitesi, Mühendislik Fakültesi, Biyomedikal Mühendisliği Bölümüen_US
dc.description.abstractGastric cancer is the sixth most common cancer and the fourth leading cause of cancer deaths worldwide. Gastric cancer presents with a more insidious onset and is most frequently discovered at an advanced stage. Early diagnosis is critical since the stage of the disease is determinant in the severity, treatment, and survival rate of cancer. In the study, the Region of Interest (RoI) was determined in histopathological images using image preprocessing techniques and signet ring cell carcinoma (SRCC) was detected with popular deep learning models VGG16, VGG19, and InceptionV3. The fine-tuning strategy was applied by customizing the last five layers of deep network models based on the target data. The parameters of accuracy, precision, recall, and F1-score were used to evaluate the model performance. Signet ring cell dataset taken from the competition "Digestive System Pathological Detection, and Segmentation Challenge 2019" was employed. When compared to results of the DigestPath2019 Grand challenge ring cell gastric cancer competition, higher accuracy rates were obtained using deep learning models with the accurate defined RoI images. VGG16 model exhibited a higher performance with accuracy of 95% and a F1-score of 95% among the models. The results obtained by the algorithm were analyzed and confirmed by the experienced pathologist.en_US
dc.identifier.citationBudak, C. ve Mençik, V. (2022). Detection of ring cell cancer in histopathological images with region of interest determined by SLIC superpixels method. Neural Computing & Application, 34(16(SI)), 13499-13512.en_US
dc.identifier.doi10.1007/s00521-022-07183-8
dc.identifier.endpage13512en_US
dc.identifier.issn0941-0643
dc.identifier.issn1433-3058
dc.identifier.issue16(SI)en_US
dc.identifier.scopus2-s2.0-85127321982
dc.identifier.scopusqualityQ1
dc.identifier.startpage13499en_US
dc.identifier.urihttps://link.springer.com/article/10.1007/s00521-022-07183-8
dc.identifier.urihttps://hdl.handle.net/11468/11512
dc.identifier.volume34en_US
dc.identifier.wosWOS:000774598500001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorBudak, Cafer
dc.institutionauthorMençik, Vasfiye
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofNeural Computing & Application
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDeep learningen_US
dc.subjectGastric canceren_US
dc.subjectRegion of Interest (RoI)en_US
dc.subjectHistopathological imagesen_US
dc.subjectDiagnosing canceren_US
dc.titleDetection of ring cell cancer in histopathological images with region of interest determined by SLIC superpixels methoden_US
dc.titleDetection of ring cell cancer in histopathological images with region of interest determined by SLIC superpixels method
dc.typeArticleen_US

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