Optimization of Convolutional Neural Network Ensemble Classifiers by Genetic Algorithms

被引：4

作者：

Molina-Cabello, Miguel A. ^{[1
]}

Accino, Cristian ^{[1
]}

Lopez-Rubio, Ezequiel ^{[1
]}

Thurnhofer-Hemsi, Karl ^{[1
]}

机构：

[1] Univ Malaga, Dept Comp Languages & Comp Sci, Bulevar Louis Pasteur 35, E-29071 Malaga, Spain

来源：

ADVANCES IN COMPUTATIONAL INTELLIGENCE, IWANN 2019, PT II | 2019年 / 11507卷

关键词：

Breast cancer classification; Medical image processing; Convolutional neural networks; SEGMENTATION;

D O I：

10.1007/978-3-030-20518-8_14

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：163 / 173

页数：11

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