Detalhes bibliográficos
| Ano de defesa: |
2009 |
| Autor(a) principal: |
SILVA, Cleriston Araújo da
 |
| Orientador(a): |
SILVA, Aristófanes Corrêa
,
PAIVA, Anselmo Cardoso de
|
| Banca de defesa: |
Não Informado pela instituição |
| Tipo de documento: |
Dissertação
|
| Tipo de acesso: |
Acesso aberto |
| Idioma: |
por |
| Instituição de defesa: |
Universidade Federal do Maranhão
|
| Programa de Pós-Graduação: |
PROGRAMA DE PÓS-GRADUAÇÃO EM ENGENHARIA DE ELETRICIDADE/CCET
|
| Departamento: |
DEPARTAMENTO DE ENGENHARIA DA ELETRICIDADE/CCET
|
| País: |
Brasil
|
| Palavras-chave em Português: |
|
| Palavras-chave em Inglês: |
|
| Área do conhecimento CNPq: |
|
| Link de acesso: |
http://tedebc.ufma.br:8080/jspui/handle/tede/1841
|
Resumo: |
The diagnosis of lung nodules has been constantly looked for by researchers as a way to minimize the high global mortality indices related to lung cancer. The usage of medical images, such as Computerized Tomography, has made possible the deepening and the improvement of techniques used to evaluate exams and provide diagnosis. This work presents a methodology for diagnosing single lung nodules that can be an aid for studies performed on similar areas and for specialists. This methodology was applied to two different image databases. The representation of the nodules was done with extraction of geometry and texture features, being the last one described through Simpson’s Index, a statistic used in Spatial Analysis and in Ecology. These features were submitted to the Support Vector Machine classifier (SVM) in two approaches: the traditional approach and the approach by using One Class. With the traditional SVM approach, we have obtained sensibility rates of 90%, specificity of 96.67% and accuracy of 95%. Using One Class SVM, the obtained rates were: sensibility of 89.7%, specificity of 89.7% and accuracy of 89.7%. |
