Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/146840
Title: Biomedical image classification based on a feature concatenation and ensemble of deep CNNs
Authors: Nguyen, Long D.
Gao, Ruihan
Lin, Dongyun
Lin, Zhipijng
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2019
Source: Nguyen, L. D., Gao, R., Lin, D. & Lin, Z. (2019). Biomedical image classification based on a feature concatenation and ensemble of deep CNNs. Journal of Ambient Intelligence and Humanized Computing. https://dx.doi.org/10.1007/s12652-019-01276-4
Journal: Journal of Ambient Intelligence and Humanized Computing
Abstract: Deep learning and more specifically Convolutional Neural Network (CNN) is a cutting edge technique which has been applied to many fields including biomedical image classification. To further improve the classification performance for biomedical images, in this paper, a feature concatenation method and a feature concatenation and ensemble method are proposed to combine several CNNs with different depths and structures. Three datasets, namely 2D Hela dataset, PAP smear dataset, and Hep-2 cell image dataset, are used as benchmarks for testing the proposed methods. It is shown from experiments that the feature concatenation and ensemble method outperforms each individual CNN, and the feature concatenation method, as well as several state-of-the-art methods in terms of classification accuracy.
URI: https://hdl.handle.net/10356/146840
ISSN: 1868-5137
DOI: 10.1007/s12652-019-01276-4
Rights: © 2019 Springer-Verlag GmbH Germany, part of Springer Nature. All rights reserved. This paper was published in Journal of Ambient Intelligence and Humanized Computing and is made available with permission of Springer-Verlag GmbH Germany, part of Springer Nature.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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