A bioinspired neural architecture search based convolutional neural network for breast cancer detection using histopathology images

Abstract The design of neural architecture to address the challenge of detecting abnormalities in histopathology images can leverage the gains made in the field of neural architecture search (NAS). The NAS model consists of a search space, search strategy and evaluation strategy. The approach suppor...

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Autores principales:	Olaide N. Oyelade, Absalom E. Ezugwu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/c0583e92148541dda89439446fe8a088
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spelling	oai:doaj.org-article:c0583e92148541dda89439446fe8a0882021-12-02T18:01:52ZA bioinspired neural architecture search based convolutional neural network for breast cancer detection using histopathology images10.1038/s41598-021-98978-72045-2322https://doaj.org/article/c0583e92148541dda89439446fe8a0882021-10-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-98978-7https://doaj.org/toc/2045-2322Abstract The design of neural architecture to address the challenge of detecting abnormalities in histopathology images can leverage the gains made in the field of neural architecture search (NAS). The NAS model consists of a search space, search strategy and evaluation strategy. The approach supports the automation of deep learning (DL) based networks such as convolutional neural networks (CNN). Automating the process of CNN architecture engineering using this approach allows for finding the best performing network for learning classification problems in specific domains and datasets. However, the engineering process of NAS is often limited by the potential solutions in search space and the search strategy. This problem often narrows the possibility of obtaining best performing networks for challenging tasks such as the classification of breast cancer in digital histopathological samples. This study proposes a NAS model with a novel search space initialization algorithm and a new search strategy. We designed a block-based stochastic categorical-to-binary (BSCB) algorithm for generating potential CNN solutions into the search space. Also, we applied and investigated the performance of a new bioinspired optimization algorithm, namely the Ebola optimization search algorithm (EOSA), for the search strategy. The evaluation strategy was achieved through computation of loss function, architectural latency and accuracy. The results obtained using images from the BACH and BreakHis databases showed that our approach obtained best performing architectures with the top-5 of the architectures yielding a significant detection rate. The top-1 CNN architecture demonstrated a state-of-the-art performance of base on classification accuracy. The NAS strategy applied in this study and the resulting candidate architecture provides researchers with the most appropriate or suitable network configuration for using digital histopathology.Olaide N. OyeladeAbsalom E. EzugwuNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-28 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Olaide N. Oyelade Absalom E. Ezugwu A bioinspired neural architecture search based convolutional neural network for breast cancer detection using histopathology images
description	Abstract The design of neural architecture to address the challenge of detecting abnormalities in histopathology images can leverage the gains made in the field of neural architecture search (NAS). The NAS model consists of a search space, search strategy and evaluation strategy. The approach supports the automation of deep learning (DL) based networks such as convolutional neural networks (CNN). Automating the process of CNN architecture engineering using this approach allows for finding the best performing network for learning classification problems in specific domains and datasets. However, the engineering process of NAS is often limited by the potential solutions in search space and the search strategy. This problem often narrows the possibility of obtaining best performing networks for challenging tasks such as the classification of breast cancer in digital histopathological samples. This study proposes a NAS model with a novel search space initialization algorithm and a new search strategy. We designed a block-based stochastic categorical-to-binary (BSCB) algorithm for generating potential CNN solutions into the search space. Also, we applied and investigated the performance of a new bioinspired optimization algorithm, namely the Ebola optimization search algorithm (EOSA), for the search strategy. The evaluation strategy was achieved through computation of loss function, architectural latency and accuracy. The results obtained using images from the BACH and BreakHis databases showed that our approach obtained best performing architectures with the top-5 of the architectures yielding a significant detection rate. The top-1 CNN architecture demonstrated a state-of-the-art performance of base on classification accuracy. The NAS strategy applied in this study and the resulting candidate architecture provides researchers with the most appropriate or suitable network configuration for using digital histopathology.
format	article
author	Olaide N. Oyelade Absalom E. Ezugwu
author_facet	Olaide N. Oyelade Absalom E. Ezugwu
author_sort	Olaide N. Oyelade
title	A bioinspired neural architecture search based convolutional neural network for breast cancer detection using histopathology images
title_short	A bioinspired neural architecture search based convolutional neural network for breast cancer detection using histopathology images
title_full	A bioinspired neural architecture search based convolutional neural network for breast cancer detection using histopathology images
title_fullStr	A bioinspired neural architecture search based convolutional neural network for breast cancer detection using histopathology images
title_full_unstemmed	A bioinspired neural architecture search based convolutional neural network for breast cancer detection using histopathology images
title_sort	bioinspired neural architecture search based convolutional neural network for breast cancer detection using histopathology images
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/c0583e92148541dda89439446fe8a088
work_keys_str_mv	AT olaidenoyelade abioinspiredneuralarchitecturesearchbasedconvolutionalneuralnetworkforbreastcancerdetectionusinghistopathologyimages AT absalomeezugwu abioinspiredneuralarchitecturesearchbasedconvolutionalneuralnetworkforbreastcancerdetectionusinghistopathologyimages AT olaidenoyelade bioinspiredneuralarchitecturesearchbasedconvolutionalneuralnetworkforbreastcancerdetectionusinghistopathologyimages AT absalomeezugwu bioinspiredneuralarchitecturesearchbasedconvolutionalneuralnetworkforbreastcancerdetectionusinghistopathologyimages
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A bioinspired neural architecture search based convolutional neural network for breast cancer detection using histopathology images

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