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|Type:||Artigo de periódico|
|Title:||Comparative Study Of Computacional Models Generated From Representations Of Colonoscopic Images: Normal Mucosal Tissues Vs Mucosal Tissues Of Colic Polyp [estudo Comparativo De Modelos Computacionais Gerados Sobre Representações De Imagens De Coloscopia: Tecido De Mucosa Normal Vs Tecido De Mucosa De Pólipo Cólico]|
|Abstract:||Purpose: to evaluate the predictive quality of computational models to differentiate colic tissues, based on Co-occorrurence Matrices (MC) representation of Coloscopic Images (IC). Materials and Methods: Image analysis and artificial intelligence methods were employed to construct computational models. Sixty seven IC images, containing polyp, were considered in this work, from which a part containing a polypus and another without it were collected given origin to 134 images. For each one of these, different MC were constructed considering five distance parameters (D = 1 to 5) and the extraction of 11 texture characteristics. With this representation, five computational models were generated based on decision trees. These models were evaluated using two techniques: (a) cross-validation and (b) contingency tables. Results: for the (a) analysis, the model with D = 3 presented the smaller average error (22.25% ± 11.85%). For the (b) analysis, models with D = 1 and 3 presented the best precision values. Conclusion: parameters D = 1 and 3 presented models with the best predictive qualities. Results showed that the constructed models were promising to be applied within decision making computational systems.|
|Appears in Collections:||Unicamp - Artigos e Outros Documentos|
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