| Revue: | Computación y sistemas |
| Base de datos: | |
| Número de sistema: | 000560626 |
| ISSN: | 1405-5546 |
| Autores: | Ochoa Montiel, Rocío1 Sossa, Humberto1 Olague, Gustavo4 Chan Ley, Mariana4 Menéndez, José4 |
| Instituciones: | 1Instituto Politécnico Nacional, Centro de Investigación en Computación, México 2Universidad Autónoma de Tlaxcala, Facultad de Ciencias Básicas Ingeniería y Tecnología, México 3Tecnológico de Monterrey, México 4Centro de Investigación Científica y de Educación Superior de Ensenada, México |
| Año: | 2021 |
| Periodo: | Oct-Dic |
| Volumen: | 25 |
| Número: | 4 |
| Paginación: | 707-718 |
| País: | México |
| Idioma: | Inglés |
| Resumen en inglés | 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. |
| Keyword: | Leukemia recognition, Symbolic learning, Brain programming, Evolutionary computer vision |
| Texte intégral: | Texto completo (Ver HTML) Texto completo (Ver PDF) |
