| Revista: | Computación y sistemas |
| Base de datos: | |
| Número de sistema: | 000560626 |
| ISSN: | 1405-5546 |
| Autores: | Ochoa Montiel, Rocío2 Sossa, Humberto1 Olague, Gustavo3 Chan Ley, Mariana3 Menéndez, José3 |
| Instituciones: | 1Instituto Politécnico Nacional, Centro de Investigación en Computación, Ciudad de México. México 2Universidad Autónoma de Tlaxcala, Facultad de Ciencias Básicas Ingeniería y Tecnología, Tlaxcala. México 3Centro de Investigación Científica y de Educación Superior de Ensenada, Ensenada, Baja California. México |
| Año: | 2021 |
| Periodo: | Oct-Dic |
| Volumen: | 25 |
| Número: | 4 |
| Paginación: | 707-718 |
| País: | México |
| Idioma: | Inglés |
| Tipo de documento: | Artículo |
| 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. |
| Disciplinas: | Ciencias de la computación |
| Palabras clave: | Procesamiento de datos |
| Keyword: | Leukemia recognition, Symbolic learning, Brain programming, Evolutionary computer vision, Data processing |
| Texto completo: | Texto completo (Ver HTML) Texto completo (Ver PDF) |
