Evolving Ensemble Models for Image Segmentation Using Enhanced Particle Swarm Optimization. / Tan, Teck Yan; Zhang, Li; Lim, Chee Peng; Fielding, Ben; Yu, Yonghong; Anderson, Emma.

In: IEEE Access, Vol. 7, 05.03.2019, p. 34004-34019.

Research output: Contribution to journalArticlepeer-review

Published
  • Teck Yan Tan
  • Li Zhang
  • Chee Peng Lim
  • Ben Fielding
  • Yonghong Yu
  • Emma Anderson

Abstract

In this paper, we propose particle swarm optimization (PSO)-enhanced ensemble deep neural networks and hybrid clustering models for skin lesion segmentation. A PSO variant is proposed, which embeds diverse search actions including simulated annealing, levy flight, helix behavior, modified PSO, and differential evolution operations with spiral search coefficients. These search actions work in a cascade manner to not only equip each individual with different search operations throughout the search process but also assign distinctive search actions to different particles simultaneously in every single iteration. The proposed PSO variant is used to optimize the learning hyper-parameters of convolutional neural networks (CNNs) and the cluster centroids of classical Fuzzy C-Means clustering respectively to overcome performance barriers. Ensemble deep networks and hybrid clustering models are subsequently constructed based on the optimized CNN and hybrid clustering segmenters for lesion segmentation. We evaluate the proposed ensemble models using three skin lesion databases, i.e., PH2, ISIC 2017, and Dermofit Image Library, and a blood cancer data set, i.e., ALL-IDB2. The empirical results indicate that our models outperform other hybrid ensemble clustering models combined with advanced PSO variants, as well as state-of-the-art deep networks in the literature for diverse challenging image segmentation tasks.
Original languageEnglish
Pages (from-to)34004-34019
Number of pages16
JournalIEEE Access
Volume7
DOIs
Publication statusPublished - 5 Mar 2019
This open access research output is licenced under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.

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