Optimization of Convolutional Neural Network Ensemble Classifiers by Genetic Algorithms-Reference-Cited by-同舟云学术

Optimization of Convolutional Neural Network Ensemble Classifiers by Genetic Algorithms

Published:2019 Issue: Volume: Page:163-173
ISSN:0302-9743
Container-title:Advances in Computational Intelligence
language:
Short-container-title:

Author:

Molina-Cabello Miguel A.,Accino Cristian,López-Rubio Ezequiel,Thurnhofer-Hemsi Karl

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-20518-8_14

Reference16 articles.

1. Caffe models trained on the images of breakhist acquired with 40x magnification factor. https://web.inf.ufpr.br/vri/databases/breast-cancer-histopathological-database-breakhis/ . Accessed 25 May 2017

2. Bagui, O.K., Zoueu, J.T.: Red blood cells counting by circular hough transform using multispectral images. J. Appl. Sci. 14(24), 3591–3594 (2014)

3. Davis, R., Boyers, S.: The role of digital image analysis in reproductive biology and medicine. Arch. Pathol. Lab. Med. 116(4), 351–363 (1992)

4. Jia, Y., et al.: Caffe: convolutional architecture for fast feature embedding. In: MM 2014 - Proceedings of the 2014 ACM Conference on Multimedia, pp. 675–678 (2014)

5. Krizhevsky, A., Sutskever, I., Hinton, G.: ImageNet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst. 25, 1097–1105 (2012)

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A Two-Stage Differential Evolutionary Algorithm for Deep Ensemble Model Generation;IEEE Transactions on Evolutionary Computation;2022

2. Feature Density as an Uncertainty Estimator Method in the Binary Classification Mammography Images Task for a Supervised Deep Learning Model;Bioinformatics and Biomedical Engineering;2022

3. Enforcing Morphological Information in Fully Convolutional Networks to Improve Cell Instance Segmentation in Fluorescence Microscopy Images;Advances in Computational Intelligence;2021