TY - GEN
T1 - Towards explainable artificial intelligence for the leukemia subtype recognition
AU - Ochoa-Montiel, Rocio
AU - Olague, Gustavo
AU - Sossa, Humberto
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - In this work, we provide a solution to the leukemia subtype recognition problem. Most approaches used for solving a variety of pattern recognition problems have a drawback: in general, they lack explainability. In this paper, we provide a solution for facing this situation. We describe a model whose stages allow deriving knowledge for solving the leukemia subtype recognition problem, are intelligible for the user. Results show that multiclass recognition is achieved from the solutions obtained by the model through multiple runs.
AB - In this work, we provide a solution to the leukemia subtype recognition problem. Most approaches used for solving a variety of pattern recognition problems have a drawback: in general, they lack explainability. In this paper, we provide a solution for facing this situation. We describe a model whose stages allow deriving knowledge for solving the leukemia subtype recognition problem, are intelligible for the user. Results show that multiclass recognition is achieved from the solutions obtained by the model through multiple runs.
KW - artificial visual cortex
KW - brain programming
KW - evolutionary vision
KW - explainable artificial intelligence
KW - leukemia recognition
UR - http://www.scopus.com/inward/record.url?scp=85130592356&partnerID=8YFLogxK
U2 - 10.1109/LA-CCI48322.2021.9769826
DO - 10.1109/LA-CCI48322.2021.9769826
M3 - Contribución a la conferencia
AN - SCOPUS:85130592356
T3 - 2021 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2021
BT - 2021 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2021
Y2 - 2 November 2021 through 4 November 2021
ER -