Computer-aided diagnosis system for lung nodules based on computed tomography using shape analysis, a genetic algorithm, and SVM.
Lung cancer is the major cause of death among patients with cancer worldwide. This work is intended to develop a methodology for the diagnosis of lung nodules using images from the Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI). The proposed methodology uses image proce...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 55; no. 8; pp. 1129 - 1147 |
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| Autores principales: | , , , , , , , , |
| Formato: | diagnostic images equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2017
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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=124485697&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 124485697 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Aug2017 vid: 55 iid: 8 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 124485697 124485697 143947513 NLM27699621 124485697 10.1007/s11517-016-1577-7 NLM27699621 124485697 ppf: 1129 ppct: 18 formats: fmt: @attributes: type: P tig: atl: Computer-aided diagnosis system for lung nodules based on computed tomography using shape analysis, a genetic algorithm, and SVM. aug: au: Carvalho Filho, Antonio Silva, Aristófanes Paiva, Anselmo Nunes, Rodolfo Gattass, Marcelo de Carvalho Filho, Antonio Oseas Silva, Aristófanes Corrêa de Paiva, Anselmo Cardoso Nunes, Rodolfo Acatauassú affil: Applied Computing Group - NCA , Federal University of Maranhão - UFMA , Av. dos Portugueses, SN, Campus do Bacanga, Bacanga São Luís 65085-580 Brazil sug: subj: Genetic Algorithms Solitary Pulmonary Nodule Pathology Tomography, X-Ray Computed Methods Solitary Pulmonary Nodule Radiographic Image Interpretation, Computer-Assisted Methods Information Science Methods Sensitivity and Specificity Radiographic Image Enhancement Methods Male Reproducibility of Results Models, Biological Female Middle Age Human Support Vector Machine Middle Aged: 45-64 years Male Female ab: Lung cancer is the major cause of death among patients with cancer worldwide. This work is intended to develop a methodology for the diagnosis of lung nodules using images from the Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI). The proposed methodology uses image processing and pattern recognition techniques. To differentiate the patterns of malignant and benign forms, we used a Minkowski functional, distance measures, representation of the vector of points measures, triangulation measures, and Feret diameters. Finally, we applied a genetic algorithm to select the best model and a support vector machine for classification. In the test stage, we applied the proposed methodology to 1405 (394 malignant and 1011 benign) nodules from the LIDC-IDRI database. The proposed methodology shows promising results for diagnosis of malignant and benign forms, achieving accuracy of 93.19 %, sensitivity of 92.75 %, and specificity of 93.33 %. The results are promising and demonstrate a good rate of correct detections using the shape features. Because early detection allows faster therapeutic intervention, and thus a more favorable prognosis for the patient, herein we propose a methodology that contributes to the area. pubtype: Academic Journal doctype: diagnostic images equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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