2026-06-07T05:22:12Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/179742026-02-03T11:50:24Zcom_10630_2254col_10630_37959

00925njm 22002777a 4500 dc Molina-Cabello, Miguel Ángel author Accino, Cristian author López-Rubio, Ezequiel author Thurnhofer-Hemsi, Karl author 2019 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. 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 https://hdl.handle.net/10630/17974 Optimization of Convolutional Neural Network ensemble classifiers by Genetic Algorithms