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Breath analysis based early gastric cancer classification from deep stacked sparse autoencoder neural network

Deep learning is an emerging tool, which is regularly used for disease diagnosis in the medical field. A new research direction has been developed for the detection of early-stage gastric cancer. The computer-aided diagnosis (CAD) systems reduce the mortality rate due to their effectiveness. In this...

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Autores principales: Aslam, Muhammad Aqeel, Xue, Cuili, Chen, Yunsheng, Zhang, Amin, Liu, Manhua, Wang, Kan, Cui, Daxiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7889910/
https://www.ncbi.nlm.nih.gov/pubmed/33597551
http://dx.doi.org/10.1038/s41598-021-83184-2
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author Aslam, Muhammad Aqeel
Xue, Cuili
Chen, Yunsheng
Zhang, Amin
Liu, Manhua
Wang, Kan
Cui, Daxiang
author_facet Aslam, Muhammad Aqeel
Xue, Cuili
Chen, Yunsheng
Zhang, Amin
Liu, Manhua
Wang, Kan
Cui, Daxiang
author_sort Aslam, Muhammad Aqeel
collection PubMed
description Deep learning is an emerging tool, which is regularly used for disease diagnosis in the medical field. A new research direction has been developed for the detection of early-stage gastric cancer. The computer-aided diagnosis (CAD) systems reduce the mortality rate due to their effectiveness. In this study, we proposed a new method for feature extraction using a stacked sparse autoencoder to extract the discriminative features from the unlabeled data of breath samples. A Softmax classifier was then integrated to the proposed method of feature extraction, to classify gastric cancer from the breath samples. Precisely, we identified fifty peaks in each spectrum to distinguish the EGC, AGC, and healthy persons. This CAD system reduces the distance between the input and output by learning the features and preserve the structure of the input data set of breath samples. The features were extracted from the unlabeled data of the breath samples. After the completion of unsupervised training, autoencoders with Softmax classifier were cascaded to develop a deep stacked sparse autoencoder neural network. In last, fine-tuning of the developed neural network was carried out with labeled training data to make the model more reliable and repeatable. The proposed deep stacked sparse autoencoder neural network architecture exhibits excellent results, with an overall accuracy of 98.7% for advanced gastric cancer classification and 97.3% for early gastric cancer detection using breath analysis. Moreover, the developed model produces an excellent result for recall, precision, and f score value, making it suitable for clinical application.
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spelling pubmed-78899102021-02-22 Breath analysis based early gastric cancer classification from deep stacked sparse autoencoder neural network Aslam, Muhammad Aqeel Xue, Cuili Chen, Yunsheng Zhang, Amin Liu, Manhua Wang, Kan Cui, Daxiang Sci Rep Article Deep learning is an emerging tool, which is regularly used for disease diagnosis in the medical field. A new research direction has been developed for the detection of early-stage gastric cancer. The computer-aided diagnosis (CAD) systems reduce the mortality rate due to their effectiveness. In this study, we proposed a new method for feature extraction using a stacked sparse autoencoder to extract the discriminative features from the unlabeled data of breath samples. A Softmax classifier was then integrated to the proposed method of feature extraction, to classify gastric cancer from the breath samples. Precisely, we identified fifty peaks in each spectrum to distinguish the EGC, AGC, and healthy persons. This CAD system reduces the distance between the input and output by learning the features and preserve the structure of the input data set of breath samples. The features were extracted from the unlabeled data of the breath samples. After the completion of unsupervised training, autoencoders with Softmax classifier were cascaded to develop a deep stacked sparse autoencoder neural network. In last, fine-tuning of the developed neural network was carried out with labeled training data to make the model more reliable and repeatable. The proposed deep stacked sparse autoencoder neural network architecture exhibits excellent results, with an overall accuracy of 98.7% for advanced gastric cancer classification and 97.3% for early gastric cancer detection using breath analysis. Moreover, the developed model produces an excellent result for recall, precision, and f score value, making it suitable for clinical application. Nature Publishing Group UK 2021-02-17 /pmc/articles/PMC7889910/ /pubmed/33597551 http://dx.doi.org/10.1038/s41598-021-83184-2 Text en © The Author(s) 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Aslam, Muhammad Aqeel
Xue, Cuili
Chen, Yunsheng
Zhang, Amin
Liu, Manhua
Wang, Kan
Cui, Daxiang
Breath analysis based early gastric cancer classification from deep stacked sparse autoencoder neural network
title Breath analysis based early gastric cancer classification from deep stacked sparse autoencoder neural network
title_full Breath analysis based early gastric cancer classification from deep stacked sparse autoencoder neural network
title_fullStr Breath analysis based early gastric cancer classification from deep stacked sparse autoencoder neural network
title_full_unstemmed Breath analysis based early gastric cancer classification from deep stacked sparse autoencoder neural network
title_short Breath analysis based early gastric cancer classification from deep stacked sparse autoencoder neural network
title_sort breath analysis based early gastric cancer classification from deep stacked sparse autoencoder neural network
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7889910/
https://www.ncbi.nlm.nih.gov/pubmed/33597551
http://dx.doi.org/10.1038/s41598-021-83184-2
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