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Ensemble learning based on classifier prediction confidence and comprehensive learning particle swarm optimisation for medical image segmentation.

Dang, Truong; Nguyen, Tien Thanh; McCall, John; Liew, Alan Wee-Chung

Authors

Alan Wee-Chung Liew



Contributors

Hisao Ishibuchi
Editor

Chee-Keong Kwoh
Editor

Ah-Hwee Tan
Editor

Dipti Srinivasan
Editor

Chunyan Miao
Editor

Anupam Trivedi
Editor

Keeley Crockett
Editor

Abstract

Segmentation, a process of partitioning an image into multiple segments to locate objects and boundaries, is considered one of the most essential medical imaging process. In recent years, Deep Neural Networks (DNN) have achieved many notable successes in medical image analysis, including image segmentation. Due to the fact that medical imaging applications require robust, reliable results, it is necessary to devise effective DNN models for medical applications. One solution is to combine multiple DNN models in an ensemble system to obtain better results than using each single DNN model. Ensemble learning is a popular machine learning technique in which multiple models are combined to improve the final results and has been widely used in medical image analysis. In this paper, we propose to measure the confidence in the prediction of each model in the ensemble system and then use an associate threshold to determine whether the confidence is acceptable or not. A segmentation model is selected based on the comparison between the confidence and its associated threshold. The optimal threshold for each segmentation model is found by using Comprehensive Learning Particle Swarm Optimisation (CLPSO), a swarm intelligence algorithm. The Dice coefficient, a popular performance metric for image segmentation, is used as the fitness criteria. The experimental results on three medical image segmentation datasets confirm that our ensemble achieves better results compared to some well-known segmentation models.

Citation

DANG, T., NGUYEN, T.T., MCCALL, J. and LIEW, A.W.-C. 2022. Ensemble learning based on classifier prediction confidence and comprehensive learning particle swarm optimisation for medical image segmentation. In Ishibuchi, H., Kwoh, C.-K., Tan, A.-H., Srinivasan, D., Miao, C., Trivedi, A. and Crockett, K. (eds.) Proceedings of the 2022 IEEE Symposium series on computational intelligence (SSCI 2022), 4-7 December 2022, Singapore. Piscataway: IEEE [online], pages 269-276. Available from: https://doi.org/10.1109/SSCI51031.2022.10022114

Conference Name 2022 IEEE Symposium series on computational intelligence (SSCI 2022)
Conference Location Singapore, Singapore
Start Date Dec 4, 2022
End Date Dec 7, 2022
Acceptance Date Sep 1, 2022
Online Publication Date Dec 7, 2022
Publication Date Jan 30, 2023
Deposit Date Feb 2, 2023
Publicly Available Date Feb 2, 2023
Publisher Institute of Electrical and Electronics Engineers
Pages 269-276
Book Title Proceedings of the 2022 IEEE Symposium series on computational intelligence (SSCI 2022)
ISBN 9781665487689
DOI https://doi.org/10.1109/SSCI51031.2022.10022114
Keywords Image segmentation; Deep learning; Ensemble selection; Ensemble method; Particle swarm optimization
Public URL https://rgu-repository.worktribe.com/output/1871687

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© 2022 IEEE.





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