Ontology-Based Approach for Liver Cancer Diagnosis and Treatment.
Liver cancer is the third deadliest cancer in the world. It characterizes a malignant tumor that develops through liver cells. The hepatocellular carcinoma (HCC) is one of these tumors. Hepatic primary cancer is the leading cause of cancer deaths. This article deals with the diagnostic process of li...
| Publicado en: | Journal of Digital Imaging Vol. 32; no. 1; pp. 116 - 131 |
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| Autores principales: | , , , , , , , , , |
| Formato: | diagnostic images pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Feb2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=134830557&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 134830557 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Feb2019 vid: 32 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 134830557 134830557 134830557 10.1007/s10278-018-0115-6 134830557 ppf: 116 ppct: 15 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Ontology-Based Approach for Liver Cancer Diagnosis and Treatment. aug: au: Messaoudi, Rim Mtibaa, Achraf Gargouri, Faiez Jaziri, Faouzi Grand-Brochier, Manuel Ali, Hawa Mohamed Chabrot, Pascal Vacavant, Antoine Amouri, Ali Fourati, Hela affil: MIRACL Laboratory, University of Sfax, Sfax, Tunisia sug: subj: Carcinoma, Hepatocellular Diagnosis Carcinoma, Hepatocellular Therapy Ontologies Neoplasm Staging Validity Precision Human Diagnostic Imaging Methods Semantic Web Image Processing, Computer Assisted Therapy, Computer Assisted Methods ab: Liver cancer is the third deadliest cancer in the world. It characterizes a malignant tumor that develops through liver cells. The hepatocellular carcinoma (HCC) is one of these tumors. Hepatic primary cancer is the leading cause of cancer deaths. This article deals with the diagnostic process of liver cancers. In order to analyze a large mass of medical data, ontologies are effective; they are efficient to improve medical image analysis used to detect different tumors and other liver lesions. We are interested in the HCC. Hence, the main purpose of this paper is to offer a new ontology-based approach modeling HCC tumors by focusing on two major aspects: the first focuses on tumor detection in medical imaging, and the second focuses on its staging by applying different classification systems. We implemented our approach in Java using Jena API. Also, we developed a prototype OntHCC by the use of semantic aspects and reasoning rules to validate our work. To show the efficiency of our work, we tested the proposed approach on real datasets. The obtained results have showed a reliable system with high accuracies of recall (76%), precision (85%), and F-measure (80%). pubtype: Academic Journal doctype: diagnostic images pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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