RT Conference Proceedings
T1 Optimization of Convolutional Neural Network ensemble classifiers by Genetic Algorithms
A1 Molina-Cabello, Miguel Ángel
A1 Accino, Cristian
A1 López-Rubio, Ezequiel
A1 Thurnhofer-Hemsi, Karl
AB Breast cancer exhibits a high mortality rate and it is the most invasive cancer in women. An analysis from histopathological images could predict this disease. In this way, computational image processing might support this task. In this work a proposal which employes deep learning convolutional neural networks is presented. Then, an ensemble of networks is considered in order to obtain an enhanced recognition performance of the system by the consensus of the networks of the ensemble. Finally, a genetic algorithm is also considered to choose the networks that belong to the ensemble. The proposal has been tested by carrying out several experiments with a set of benchmark images.
PB Springer
YR 2019
FD 2019
LK https://hdl.handle.net/10630/17974
UL https://hdl.handle.net/10630/17974
LA eng
NO Molina-Cabello M.A., Accino C., López-Rubio E., Thurnhofer-Hemsi K. (2019) Optimization of Convolutional Neural Network Ensemble Classifiers by Genetic Algorithms. In: Rojas I., Joya G., Catala A. (eds) Advances in Computational Intelligence. IWANN 2019. Lecture Notes in Computer Science, vol 11507. Springer, Cham
NO Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 19 ene 2026