@inproceedings { , title = {Weighted ensemble of deep learning models based on comprehensive learning particle swarm optimization for medical image segmentation.}, abstract = {In recent years, deep learning has rapidly become a 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.}, conference = {2021 IEEE (Institute of electrical and electronics engineers) Congress on evolutionary computation (CEC 2021)}, doi = {10.1109/CEC45853.2021.9504929}, isbn = {9781728183923}, note = {INFO COMPLETE (Info via IEEExplore alert 12/8/2021 LM) PERMISSION GRANTED (version = AAM; embargo = none; licence = pub own; POLICY = https://conferences.ieeeauthorcenter.ieee.org/author-ethics/guidelines-and-policies/post-publication-policies/\#accepted ) DOCUMENT READY (AAM rec'd from contact 13/8/2021 LM) ADDITIONAL INFO - Contact: Carlos Moreno-Garcia; Troung Dang; Thanh Nguyen; Eyad Elyan; John McCall Set Statement - (© 20XX IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.)}, pages = {744-751}, publicationstatus = {Published}, publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, url = {https://rgu-repository.worktribe.com/output/1406165}, keyword = {Interactive Machine Vision, Net Zero Marine Operations, Health & Wellbeing, Living in a Digital World, Image segmentation, Deep neural networks, Ensemble learning, Ensemble method, Particle swarm optimization}, year = {2021}, author = {Dang, Truong and Nguyen, Tien Thanh and Moreno-García, Carlos Francisco and Elyan, Eyad and McCall, John} }