Based on improved deep convolutional neural network model pneumonia image classification

Pneumonia remains the leading infectious cause of death in children under the age of five, killing about 700,000 children each year and affecting 7% of the world’s population. X-ray images of lung become the key to the diagnosis of this disease, skilled doctors in the diagnosis of a certain degree o...

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Autores principales:	Lingzhi Kong, Jinyong Cheng
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/2ae8d7d192e3463c95856f20c1dc9fcc
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spelling	oai:doaj.org-article:2ae8d7d192e3463c95856f20c1dc9fcc2021-11-11T07:14:35ZBased on improved deep convolutional neural network model pneumonia image classification1932-6203https://doaj.org/article/2ae8d7d192e3463c95856f20c1dc9fcc2021-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8568342/?tool=EBIhttps://doaj.org/toc/1932-6203Pneumonia remains the leading infectious cause of death in children under the age of five, killing about 700,000 children each year and affecting 7% of the world’s population. X-ray images of lung become the key to the diagnosis of this disease, skilled doctors in the diagnosis of a certain degree of subjectivity, if the use of computer-aided medical diagnosis to automatically detect lung abnormalities, will improve the accuracy of diagnosis. This research aims to introduce a deep learning technology based on the combination of Xception neural network and long-term short-term memory (LSTM), which can realize automatic diagnosis of patients with pneumonia in X-ray images. First, the model uses the Xception network to extract the deep features of the data, passes the extracted features to the LSTM, and then the LSTM detects the extracted features, and finally selects the most needed features. Secondly, in the training set samples, the traditional cross-entropy loss cannot more balance the mismatch between categories. Therefore, this research combines Pearson’s feature selection ideas, fusion of the correlation between the two loss functions, and optimizes the problem. The experimental results show that the accuracy rate of this paper is 96%, the receiver operator characteristic curve accuracy rate is 99%, the precision rate is 98%, the recall rate is 91%, and the F1 score accuracy rate is 94%. Compared with the existing technical methods, the research has achieved expected results on the currently available datasets. And assist doctors to provide higher reliability in the classification task of childhood pneumonia.Lingzhi KongJinyong ChengPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Lingzhi Kong Jinyong Cheng Based on improved deep convolutional neural network model pneumonia image classification
description	Pneumonia remains the leading infectious cause of death in children under the age of five, killing about 700,000 children each year and affecting 7% of the world’s population. X-ray images of lung become the key to the diagnosis of this disease, skilled doctors in the diagnosis of a certain degree of subjectivity, if the use of computer-aided medical diagnosis to automatically detect lung abnormalities, will improve the accuracy of diagnosis. This research aims to introduce a deep learning technology based on the combination of Xception neural network and long-term short-term memory (LSTM), which can realize automatic diagnosis of patients with pneumonia in X-ray images. First, the model uses the Xception network to extract the deep features of the data, passes the extracted features to the LSTM, and then the LSTM detects the extracted features, and finally selects the most needed features. Secondly, in the training set samples, the traditional cross-entropy loss cannot more balance the mismatch between categories. Therefore, this research combines Pearson’s feature selection ideas, fusion of the correlation between the two loss functions, and optimizes the problem. The experimental results show that the accuracy rate of this paper is 96%, the receiver operator characteristic curve accuracy rate is 99%, the precision rate is 98%, the recall rate is 91%, and the F1 score accuracy rate is 94%. Compared with the existing technical methods, the research has achieved expected results on the currently available datasets. And assist doctors to provide higher reliability in the classification task of childhood pneumonia.
format	article
author	Lingzhi Kong Jinyong Cheng
author_facet	Lingzhi Kong Jinyong Cheng
author_sort	Lingzhi Kong
title	Based on improved deep convolutional neural network model pneumonia image classification
title_short	Based on improved deep convolutional neural network model pneumonia image classification
title_full	Based on improved deep convolutional neural network model pneumonia image classification
title_fullStr	Based on improved deep convolutional neural network model pneumonia image classification
title_full_unstemmed	Based on improved deep convolutional neural network model pneumonia image classification
title_sort	based on improved deep convolutional neural network model pneumonia image classification
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/2ae8d7d192e3463c95856f20c1dc9fcc
work_keys_str_mv	AT lingzhikong basedonimproveddeepconvolutionalneuralnetworkmodelpneumoniaimageclassification AT jinyongcheng basedonimproveddeepconvolutionalneuralnetworkmodelpneumoniaimageclassification
_version_	1718439383974019072

Based on improved deep convolutional neural network model pneumonia image classification

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