Convolutional neural network with group theory and random selection particle swarm optimizer for enhancing cancer image classification

As an epitome of deep learning, convolutional neural network (CNN) has shown its advantages in solving many real-world problems. Successful CNN applications on medical prognosis and diagnosis have been achieved in recent years. Their common goal is to recognize the insights from the subtle details f...

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Autores principales:	Kun Lan, Gloria Li, Yang Jie, Rui Tang, Liansheng Liu, Simon Fong
Formato:	article
Lenguaje:	EN
Publicado:	AIMS Press 2021
Materias:	convolutional neural network metaheuristic particle swarm optimization group theory random selection symmetry breast and lung cancer image classification Biotechnology TP248.13-248.65 Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/394e106986ed436996a3ca00e0995af7
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id	oai:doaj.org-article:394e106986ed436996a3ca00e0995af7
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spelling	oai:doaj.org-article:394e106986ed436996a3ca00e0995af72021-11-09T02:24:36ZConvolutional neural network with group theory and random selection particle swarm optimizer for enhancing cancer image classification10.3934/mbe.20212811551-0018https://doaj.org/article/394e106986ed436996a3ca00e0995af72021-06-01T00:00:00Zhttps://www.aimspress.com/article/doi/10.3934/mbe.2021281?viewType=HTMLhttps://doaj.org/toc/1551-0018As an epitome of deep learning, convolutional neural network (CNN) has shown its advantages in solving many real-world problems. Successful CNN applications on medical prognosis and diagnosis have been achieved in recent years. Their common goal is to recognize the insights from the subtle details from medical images by building a suitable CNN model with maximum accuracy and minimum error. The CNN performance is extremely sensitive to the parameter tuning for any given network structure. To approach this concern, a novel self-tuning CNN model is proposed with a significant characteristic of having a metaheuristic-based optimizer. The most optimal set of parameters is often found via our proposed method, namely group theory and random selection-based particle swarm optimization (GTRS-PSO). The insights of symmetric essentials of model structure and parameter correlation are extracted, followed by the hierarchical partitioning of parameter space, and four operators on those partitions are designed for moving neighborhoods and formulating the swarm topology accordingly. The parameters are updated by a random selection strategy at each interval of partitions during the search process. Preliminary experiments over two radiology image datasets: breast cancer and lung cancer, are conducted for a comprehensive comparison of GTRS-PSO versus other optimization algorithms. The results show that CNN with GTRS-PSO optimizer can achieve the best performance for cancer image classifications, especially when there are symmetric components inside the data properties and model structures.Kun LanGloria LiYang JieRui TangLiansheng Liu Simon Fong AIMS Pressarticleconvolutional neural networkmetaheuristicparticle swarm optimizationgroup theoryrandom selectionsymmetrybreast and lung cancerimage classificationBiotechnologyTP248.13-248.65MathematicsQA1-939ENMathematical Biosciences and Engineering, Vol 18, Iss 5, Pp 5573-5591 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	convolutional neural network metaheuristic particle swarm optimization group theory random selection symmetry breast and lung cancer image classification Biotechnology TP248.13-248.65 Mathematics QA1-939
spellingShingle	convolutional neural network metaheuristic particle swarm optimization group theory random selection symmetry breast and lung cancer image classification Biotechnology TP248.13-248.65 Mathematics QA1-939 Kun Lan Gloria Li Yang Jie Rui Tang Liansheng Liu Simon Fong Convolutional neural network with group theory and random selection particle swarm optimizer for enhancing cancer image classification
description	As an epitome of deep learning, convolutional neural network (CNN) has shown its advantages in solving many real-world problems. Successful CNN applications on medical prognosis and diagnosis have been achieved in recent years. Their common goal is to recognize the insights from the subtle details from medical images by building a suitable CNN model with maximum accuracy and minimum error. The CNN performance is extremely sensitive to the parameter tuning for any given network structure. To approach this concern, a novel self-tuning CNN model is proposed with a significant characteristic of having a metaheuristic-based optimizer. The most optimal set of parameters is often found via our proposed method, namely group theory and random selection-based particle swarm optimization (GTRS-PSO). The insights of symmetric essentials of model structure and parameter correlation are extracted, followed by the hierarchical partitioning of parameter space, and four operators on those partitions are designed for moving neighborhoods and formulating the swarm topology accordingly. The parameters are updated by a random selection strategy at each interval of partitions during the search process. Preliminary experiments over two radiology image datasets: breast cancer and lung cancer, are conducted for a comprehensive comparison of GTRS-PSO versus other optimization algorithms. The results show that CNN with GTRS-PSO optimizer can achieve the best performance for cancer image classifications, especially when there are symmetric components inside the data properties and model structures.
format	article
author	Kun Lan Gloria Li Yang Jie Rui Tang Liansheng Liu Simon Fong
author_facet	Kun Lan Gloria Li Yang Jie Rui Tang Liansheng Liu Simon Fong
author_sort	Kun Lan
title	Convolutional neural network with group theory and random selection particle swarm optimizer for enhancing cancer image classification
title_short	Convolutional neural network with group theory and random selection particle swarm optimizer for enhancing cancer image classification
title_full	Convolutional neural network with group theory and random selection particle swarm optimizer for enhancing cancer image classification
title_fullStr	Convolutional neural network with group theory and random selection particle swarm optimizer for enhancing cancer image classification
title_full_unstemmed	Convolutional neural network with group theory and random selection particle swarm optimizer for enhancing cancer image classification
title_sort	convolutional neural network with group theory and random selection particle swarm optimizer for enhancing cancer image classification
publisher	AIMS Press
publishDate	2021
url	https://doaj.org/article/394e106986ed436996a3ca00e0995af7
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Convolutional neural network with group theory and random selection particle swarm optimizer for enhancing cancer image classification

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