RT Conference Proceedings
T1 Histopathological image analysis for breast cancer diagnosis by ensembles of convolutional neural networks and genetic algorithms
A1 Molina-Cabello, Miguel Ángel
A1 Rodríguez Rodríguez, José Antonio
A1 Thurnhofer Hemsi, Karl
A1 López-Rubio, Ezequiel
K1 Mamas - Cáncer
AB One of the most invasive cancer types which affect women is breast cancer. Unfortunately, it exhibits a high mortalityrate. Automated histopathological image analysis can help to diagnose the disease. Therefore, computer aided diagnosis byintelligent image analysis can help in the diagnosis tasks associated with this disease. Here we propose an automated system forhistopathological image analysis that is based on deep learning neural networks with convolutional layers. Rather than a singlenetwork, an ensemble of them is built so as to attain higher recognition rates, which are obtained by computing a consensusdecision from the individual networks of the ensemble. A final step involves the optimization of the set of networks that areincluded in the ensemble by a genetic algorithm. Experimental results are provided with a set of benchmark images, withfavorable outcomes.
YR 2021
FD 2021-07
LK https://hdl.handle.net/10630/22693
UL https://hdl.handle.net/10630/22693
LA eng
NO Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 4 mar 2026