| Título |
Symbolic Learning using Brain Programming for the Recognition of Leukemia Images |
| Tipo |
Revista |
| Sub-tipo |
CONACYT |
| Descripción |
Computación y Sistemas |
| Resumen |
In this work, We propose an approach of symbolic learning for the recognition of leukemia images. Image recognition for cancer detection is often a subjective problem due to different interpretations by experts of the medical area. Feature extraction is a critical step in image recognition, and current automatic approaches are unintelligible since they need to be adapted to different image domains. We propose the paradigm of brain programming as a symbolic learning approach to address aspects involved in the derivation of knowledge that allows us to recognize subtypes of leukemia in color images. Experimental results provide evidence that the multi-class recognition task is achieved through the solutions discovered from multiples runs of the bioinspired model. © 2021 Instituto Politecnico Nacional. All rights reserved. |
| Observaciones |
DOI In this work, We propose an approach of symbolic learning for the recognition of leukemia images. Image recognition for cancer detection is often a subjective problem due to different interpretations by experts of the medical area. Feature extraction is a critical step in image recognition, and current automatic approaches are unintelligible since they need to be adapted to different image domains. We propose the paradigm of brain programming as a symbolic learning approach to address aspects involved in the derivation of knowledge that allows us to recognize subtypes of leukemia in color images. Experimental results provide evidence that the multi-class recognition task is achieved through the solutions discovered from multiples runs of the bioinspired model. © 2021 Instituto Politecnico Nacional. All rights reserved. |
| Lugar |
Ciudad de México |
| País |
Mexico |
| No. de páginas |
707-718 |
| Vol. / Cap. |
v. 25 no. 4 |
| Inicio |
2021-10-01 |
| Fin |
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| ISBN/ISSN |
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