Nuclear atypia grading in breast cancer histopathological images based on CNN feature extraction and LSTM classification

Abstract Early diagnosis of breast cancer, the most common disease among women around the world, increases the chance of treatment and is highly important. Nuclear atypia grading in histopathological images plays an important role in the final diagnosis and grading of breast cancer. Grading images b...

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Autores principales:	Sanaz Karimi Jafarbigloo, Habibollah Danyali
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	breast cancer CNN histopathological image LSTM networks nuclear atypia Computational linguistics. Natural language processing P98-98.5 Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/b41f54d631924deb839c3c22f8f18f36
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spelling	oai:doaj.org-article:b41f54d631924deb839c3c22f8f18f362021-11-17T03:12:43ZNuclear atypia grading in breast cancer histopathological images based on CNN feature extraction and LSTM classification2468-232210.1049/cit2.12061https://doaj.org/article/b41f54d631924deb839c3c22f8f18f362021-12-01T00:00:00Zhttps://doi.org/10.1049/cit2.12061https://doaj.org/toc/2468-2322Abstract Early diagnosis of breast cancer, the most common disease among women around the world, increases the chance of treatment and is highly important. Nuclear atypia grading in histopathological images plays an important role in the final diagnosis and grading of breast cancer. Grading images by pathologists is a time consuming and subjective task. Therefore, the existence of a computer‐aided system for nuclear atypia grading is very useful and necessary. In this study, two automatic systems for grading nuclear atypia in breast cancer histopathological images based on deep learning methods are proposed. A patch‐based approach is introduced due to the large size of the histopathological images and restriction of the training data. In the proposed system I, the most important patches in the image are detected first and then a three‐hidden‐layer convolutional neural network (CNN) is designed and trained for feature extraction and to classify the patches individually. The proposed system II is based on a combination of the CNN for feature extraction and a two‐layer Long short‐term memory (LSTM) network for classification. The LSTM network is utilised to consider all patches of an image simultaneously for image grading. The simulation results show the efficiency of the proposed systems for automatic nuclear atypia grading and outperform the current related studies in the literature.Sanaz Karimi JafarbiglooHabibollah DanyaliWileyarticlebreast cancerCNNhistopathological imageLSTM networksnuclear atypiaComputational linguistics. Natural language processingP98-98.5Computer softwareQA76.75-76.765ENCAAI Transactions on Intelligence Technology, Vol 6, Iss 4, Pp 426-439 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	breast cancer CNN histopathological image LSTM networks nuclear atypia Computational linguistics. Natural language processing P98-98.5 Computer software QA76.75-76.765
spellingShingle	breast cancer CNN histopathological image LSTM networks nuclear atypia Computational linguistics. Natural language processing P98-98.5 Computer software QA76.75-76.765 Sanaz Karimi Jafarbigloo Habibollah Danyali Nuclear atypia grading in breast cancer histopathological images based on CNN feature extraction and LSTM classification
description	Abstract Early diagnosis of breast cancer, the most common disease among women around the world, increases the chance of treatment and is highly important. Nuclear atypia grading in histopathological images plays an important role in the final diagnosis and grading of breast cancer. Grading images by pathologists is a time consuming and subjective task. Therefore, the existence of a computer‐aided system for nuclear atypia grading is very useful and necessary. In this study, two automatic systems for grading nuclear atypia in breast cancer histopathological images based on deep learning methods are proposed. A patch‐based approach is introduced due to the large size of the histopathological images and restriction of the training data. In the proposed system I, the most important patches in the image are detected first and then a three‐hidden‐layer convolutional neural network (CNN) is designed and trained for feature extraction and to classify the patches individually. The proposed system II is based on a combination of the CNN for feature extraction and a two‐layer Long short‐term memory (LSTM) network for classification. The LSTM network is utilised to consider all patches of an image simultaneously for image grading. The simulation results show the efficiency of the proposed systems for automatic nuclear atypia grading and outperform the current related studies in the literature.
format	article
author	Sanaz Karimi Jafarbigloo Habibollah Danyali
author_facet	Sanaz Karimi Jafarbigloo Habibollah Danyali
author_sort	Sanaz Karimi Jafarbigloo
title	Nuclear atypia grading in breast cancer histopathological images based on CNN feature extraction and LSTM classification
title_short	Nuclear atypia grading in breast cancer histopathological images based on CNN feature extraction and LSTM classification
title_full	Nuclear atypia grading in breast cancer histopathological images based on CNN feature extraction and LSTM classification
title_fullStr	Nuclear atypia grading in breast cancer histopathological images based on CNN feature extraction and LSTM classification
title_full_unstemmed	Nuclear atypia grading in breast cancer histopathological images based on CNN feature extraction and LSTM classification
title_sort	nuclear atypia grading in breast cancer histopathological images based on cnn feature extraction and lstm classification
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/b41f54d631924deb839c3c22f8f18f36
work_keys_str_mv	AT sanazkarimijafarbigloo nuclearatypiagradinginbreastcancerhistopathologicalimagesbasedoncnnfeatureextractionandlstmclassification AT habibollahdanyali nuclearatypiagradinginbreastcancerhistopathologicalimagesbasedoncnnfeatureextractionandlstmclassification
_version_	1718426010699956224

Nuclear atypia grading in breast cancer histopathological images based on CNN feature extraction and LSTM classification

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