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Weighted ensemble of deep learning models based on comprehensive learning particle swarm optimization for medical image segmentation.

Dang, Truong; Nguyen, Tien Thanh; Moreno-Garcia, Carlos Francisco; Elyan, Eyad; McCall, John

Authors

Truong Dang



Abstract

In recent years, deep learning has rapidly becomea method of choice for segmentation of medical images. Deep neural architectures such as UNet and FPN have achieved high performances on many medical datasets. However, medical image analysis algorithms are required to be reliable, robust,and accurate for clinical applications which can be difficult to achieve for some single deep learning methods. In this study, we introduce an ensemble of classifiers for semantic segmentation of medical images. The ensemble of classifiers here is a set of various deep learning-based classifiers, aiming to achieve better performance than using a single classifier. We propose a weighted ensemble method in which the weighted sum of segmentation outputs by classifiers is used to choose the final segmentation decision. We use a swarm intelligence algorithm namely Comprehensive Learning Particle Swarm Optimization to optimize the combining weights. Dice coefficient, a popular performance metric for image segmentation, is used as the fitness criteria. Experiments conducted on some medical datasets of the CAMUS competition on cardiographic image segmentation show that our method achieves better results than both the constituent segmentation models and the reported model of the CAMUS competition.

Citation

DANG, T., NGUYEN, T.T., MORENO-GARCIA, C.F., ELYAN, E. and MCCALL, J. 2021. Weighted ensemble of deep learning models based on comprehensive learning particle swarm optimization for medical image segmentation. In Proceeding of 2021 IEEE (Institute of electrical and electronics engineers) Congress on evolutionary computation (CEC 2021), 28 June - 1 July 2021, [virtual conference]. Piscataway: IEEE [online], pages 744-751. Available from: https://doi.org/10.1109/CEC45853.2021.9504929

Conference Name 2021 IEEE (Institute of electrical and electronics engineers) Congress on evolutionary computation (CEC 2021)
Conference Location [virtual conference]
Start Date Jun 28, 2021
End Date Jul 1, 2021
Acceptance Date Feb 23, 2021
Online Publication Date Jul 1, 2021
Publication Date Aug 9, 2021
Deposit Date Aug 16, 2021
Publicly Available Date Aug 16, 2021
Publisher Institute of Electrical and Electronics Engineers
ISBN 9781728183930
DOI https://doi.org/10.1109/cec45853.2021.9504929
Keywords Image segmentation; Deep neural networks; Ensemble learning; Ensemble method; Particle swarm optimization
Public URL https://rgu-repository.worktribe.com/output/1406165

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