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

Abstract 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...

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Autores principales:	Muhammad Aqeel Aslam, Cuili Xue, Yunsheng Chen, Amin Zhang, Manhua Liu, Kan Wang, Daxiang Cui
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/6edaf71c76934a3491833917528d380d
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spelling	oai:doaj.org-article:6edaf71c76934a3491833917528d380d2021-12-02T10:54:14ZBreath analysis based early gastric cancer classification from deep stacked sparse autoencoder neural network10.1038/s41598-021-83184-22045-2322https://doaj.org/article/6edaf71c76934a3491833917528d380d2021-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-83184-2https://doaj.org/toc/2045-2322Abstract 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.Muhammad Aqeel AslamCuili XueYunsheng ChenAmin ZhangManhua LiuKan WangDaxiang CuiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Muhammad Aqeel Aslam Cuili Xue Yunsheng Chen Amin Zhang Manhua Liu Kan Wang Daxiang Cui Breath analysis based early gastric cancer classification from deep stacked sparse autoencoder neural network
description	Abstract 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.
format	article
author	Muhammad Aqeel Aslam Cuili Xue Yunsheng Chen Amin Zhang Manhua Liu Kan Wang Daxiang Cui
author_facet	Muhammad Aqeel Aslam Cuili Xue Yunsheng Chen Amin Zhang Manhua Liu Kan Wang Daxiang Cui
author_sort	Muhammad Aqeel Aslam
title	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_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_sort	breath analysis based early gastric cancer classification from deep stacked sparse autoencoder neural network
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/6edaf71c76934a3491833917528d380d
work_keys_str_mv	AT muhammadaqeelaslam breathanalysisbasedearlygastriccancerclassificationfromdeepstackedsparseautoencoderneuralnetwork AT cuilixue breathanalysisbasedearlygastriccancerclassificationfromdeepstackedsparseautoencoderneuralnetwork AT yunshengchen breathanalysisbasedearlygastriccancerclassificationfromdeepstackedsparseautoencoderneuralnetwork AT aminzhang breathanalysisbasedearlygastriccancerclassificationfromdeepstackedsparseautoencoderneuralnetwork AT manhualiu breathanalysisbasedearlygastriccancerclassificationfromdeepstackedsparseautoencoderneuralnetwork AT kanwang breathanalysisbasedearlygastriccancerclassificationfromdeepstackedsparseautoencoderneuralnetwork AT daxiangcui breathanalysisbasedearlygastriccancerclassificationfromdeepstackedsparseautoencoderneuralnetwork
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Breath analysis based early gastric cancer classification from deep stacked sparse autoencoder neural network

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