Aprendizado de máquina simbólico e técnicas fractais para caracterizar rejeição em biópsia miocárdica
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Data
2013-03-26
Orientador
Coorientador
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Curso de graduação
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Trabalho apresentado em evento
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Acesso aberto
Resumo
This work combines symbolic machine learning and multiscale fractal techniques to generate models that characterize cellular rejection in myocardial biopsies and that can base a diagnosis support system. The models express the knowledge by the features threshold, fractal dimension, lacunarity, number of clusters, spatial percolation and percolation probability, all obtained with myocardial biopsies processing. Models were evaluated and the most significant was the one generated by the C4.5 algorithm for the features spatial percolation and number of clusters. The result is relevant and contributes to the specialized literature since it determines a standard diagnosis protocol. © 2013 Springer.
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Palavras-chave
multiscale fractal techniques , myocardial biopsies images , symbolic machine learning , C4.5 algorithm , Diagnosis support systems , Lacunarity , Multiscale fractals , Number of clusters , Percolation probability , Symbolic machine learning , Biomedical engineering , Fractal dimension , Learning systems , Solvents , Biopsy
Idioma
Português
Como citar
5th Latin American Congress on Biomedical Engineering (claib 2011): Sustainable Technologies For the Health of All, Pts 1 and 2. New York: Springer, v. 33, n. 1-2, p. 272-275, 2013.