Classification of Benign and Malignant Lung Nodules Based on Deep Convolutional Network Feature Extraction

With the rapid development of detection technology, CT imaging technology has been widely used in the early clinical diagnosis of lung nodules. However, accurate assessment of the nature of the nodule remains a challenging task due to the subjective nature of the radiologist. With the increasing amo...

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Autores principales:	Enhui Lv, Wenfeng Liu, Pengbo Wen, Xingxing Kang
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Medicine (General) R5-920 Medical technology R855-855.5
Acceso en línea:	https://doaj.org/article/30f68bc4d4f742dc9ea359c207e69527
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spelling	oai:doaj.org-article:30f68bc4d4f742dc9ea359c207e695272021-11-08T02:35:42ZClassification of Benign and Malignant Lung Nodules Based on Deep Convolutional Network Feature Extraction2040-230910.1155/2021/8769652https://doaj.org/article/30f68bc4d4f742dc9ea359c207e695272021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/8769652https://doaj.org/toc/2040-2309With the rapid development of detection technology, CT imaging technology has been widely used in the early clinical diagnosis of lung nodules. However, accurate assessment of the nature of the nodule remains a challenging task due to the subjective nature of the radiologist. With the increasing amount of publicly available lung image data, it has become possible to use convolutional neural networks for benign and malignant classification of lung nodules. However, as the network depth increases, network training methods based on gradient descent usually lead to gradient dispersion. Therefore, we propose a novel deep convolutional network approach to classify the benignity and malignancy of lung nodules. Firstly, we segmented, extracted, and performed zero-phase component analysis whitening on images of lung nodules. Then, a multilayer perceptron was introduced into the structure to construct a deep convolutional network. Finally, the minibatch stochastic gradient descent method with a momentum coefficient is used to fine-tune the deep convolutional network to avoid the gradient dispersion. The 750 lung nodules in the lung image database are used for experimental verification. Classification accuracy of the proposed method can reach 96.0%. The experimental results show that the proposed method can provide an objective and efficient aid to solve the problem of classifying benign and malignant lung nodules in medical images.Enhui LvWenfeng LiuPengbo WenXingxing KangHindawi LimitedarticleMedicine (General)R5-920Medical technologyR855-855.5ENJournal of Healthcare Engineering, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine (General) R5-920 Medical technology R855-855.5
spellingShingle	Medicine (General) R5-920 Medical technology R855-855.5 Enhui Lv Wenfeng Liu Pengbo Wen Xingxing Kang Classification of Benign and Malignant Lung Nodules Based on Deep Convolutional Network Feature Extraction
description	With the rapid development of detection technology, CT imaging technology has been widely used in the early clinical diagnosis of lung nodules. However, accurate assessment of the nature of the nodule remains a challenging task due to the subjective nature of the radiologist. With the increasing amount of publicly available lung image data, it has become possible to use convolutional neural networks for benign and malignant classification of lung nodules. However, as the network depth increases, network training methods based on gradient descent usually lead to gradient dispersion. Therefore, we propose a novel deep convolutional network approach to classify the benignity and malignancy of lung nodules. Firstly, we segmented, extracted, and performed zero-phase component analysis whitening on images of lung nodules. Then, a multilayer perceptron was introduced into the structure to construct a deep convolutional network. Finally, the minibatch stochastic gradient descent method with a momentum coefficient is used to fine-tune the deep convolutional network to avoid the gradient dispersion. The 750 lung nodules in the lung image database are used for experimental verification. Classification accuracy of the proposed method can reach 96.0%. The experimental results show that the proposed method can provide an objective and efficient aid to solve the problem of classifying benign and malignant lung nodules in medical images.
format	article
author	Enhui Lv Wenfeng Liu Pengbo Wen Xingxing Kang
author_facet	Enhui Lv Wenfeng Liu Pengbo Wen Xingxing Kang
author_sort	Enhui Lv
title	Classification of Benign and Malignant Lung Nodules Based on Deep Convolutional Network Feature Extraction
title_short	Classification of Benign and Malignant Lung Nodules Based on Deep Convolutional Network Feature Extraction
title_full	Classification of Benign and Malignant Lung Nodules Based on Deep Convolutional Network Feature Extraction
title_fullStr	Classification of Benign and Malignant Lung Nodules Based on Deep Convolutional Network Feature Extraction
title_full_unstemmed	Classification of Benign and Malignant Lung Nodules Based on Deep Convolutional Network Feature Extraction
title_sort	classification of benign and malignant lung nodules based on deep convolutional network feature extraction
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/30f68bc4d4f742dc9ea359c207e69527
work_keys_str_mv	AT enhuilv classificationofbenignandmalignantlungnodulesbasedondeepconvolutionalnetworkfeatureextraction AT wenfengliu classificationofbenignandmalignantlungnodulesbasedondeepconvolutionalnetworkfeatureextraction AT pengbowen classificationofbenignandmalignantlungnodulesbasedondeepconvolutionalnetworkfeatureextraction AT xingxingkang classificationofbenignandmalignantlungnodulesbasedondeepconvolutionalnetworkfeatureextraction
_version_	1718443171547971584

Classification of Benign and Malignant Lung Nodules Based on Deep Convolutional Network Feature Extraction

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