2026-05-27T12:55:03Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/226932026-02-03T12:03:56Zcom_10630_2254col_10630_37959

Molina-Cabello, Miguel Ángel Rodríguez Rodríguez, José Antonio Thurnhofer Hemsi, Karl López-Rubio, Ezequiel 2021-07-23T11:32:54Z 2021-07-23T11:32:54Z 2021-07 https://hdl.handle.net/10630/22693 One of the most invasive cancer types which affect women is breast cancer. Unfortunately, it exhibits a high mortality rate. Automated histopathological image analysis can help to diagnose the disease. Therefore, computer aided diagnosis by intelligent image analysis can help in the diagnosis tasks associated with this disease. Here we propose an automated system for histopathological image analysis that is based on deep learning neural networks with convolutional layers. Rather than a single network, an ensemble of them is built so as to attain higher recognition rates, which are obtained by computing a consensus decision from the individual networks of the ensemble. A final step involves the optimization of the set of networks that are included in the ensemble by a genetic algorithm. Experimental results are provided with a set of benchmark images, with favorable outcomes. eng open access Mamas - Cáncer Histopathological image analysis for breast cancer diagnosis by ensembles of convolutional neural networks and genetic algorithms conference output