Intelligent optic disc segmentation using improved particle swarm optimization and evolving ensemble models. / Zhang, Li; Lim, Chee Peng.

In: Applied Soft Computing, Vol. 92, 106328, 07.2020.

Research output: Contribution to journalArticlepeer-review

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Abstract

In this research, we propose Particle Swarm Optimization (PSO)-enhanced ensemble deep neural networks for optic disc (OD) segmentation using retinal images. An improved PSO algorithm with six search mechanisms to diversify the search process is introduced. It consists of an accelerated super-ellipse action, a refined super-ellipse operation, a modified PSO operation, a random leader-based search operation, an average leader-based search operation and a spherical random walk mechanism for swarm leader enhancement. Owing to the superior segmentation capabilities of Mask R-CNN, transfer learning with a PSO-based hyper-parameter identification method is employed to generate the fine-tuned segmenters for OD segmentation. Specifically, we optimize the learning parameters, which include the learning rate and momentum of the transfer learning process, using the proposed PSO algorithm. To overcome the bias of single networks, an ensemble segmentation model is constructed. It incorporates the results of distinctive base segmenters using a pixel-level majority voting mechanism to generate the final segmentation outcome. The proposed ensemble network is evaluated using the Messidor and Drions data sets and is found to significantly outperform other deep ensemble networks and hybrid ensemble clustering models that are incorporated with both the original and state-of-the-art PSO variants. Additionally, the proposed method statistically outperforms existing studies on OD segmentation and other search methods for solving diverse unimodal and multimodal benchmark optimization functions and the detection of Diabetic Macular Edema.
Original languageEnglish
Article number106328
JournalApplied Soft Computing
Volume92
Early online date20 Apr 2020
DOIs
Publication statusPublished - Jul 2020
This open access research output is licenced under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.

ID: 43379638