Semantic characteristic grading of pulmonary nodules based on deep neural networks.
Semantic characteristic grading of pulmonary nodules based on deep neural networks.
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DOI：
10.1186/s12880-023-01112-4
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发表时间：
2023-10-13
影响因子：
2.7
通讯作者：
中科院分区：
文献类型：
--
作者：
准确的语义特征分级有助于放射科医生确定肺结节恶性的可能性。然而，由于肺结节的复杂性和多样性，评估语义特征（SC）是一项困难的任务。本文首先分析了肺结节的一组重要语义特征，并利用Pearson相关性方法提取了与肺结节恶性程度相关的重要SC。然后，我们提出了三种基于深度信念网络（DBN）和多分支卷积神经网络（CNN）分类器MBCNN的自动SC分级模型。第一个DBN模型以灰度和二值结节图像作为输入，第二个DBN模型以灰度结节图像和从肺结节提取的72个特征作为输入。实验结果表明，本文算法在语义特征分级方面取得了令人满意的效果.特别是，MBCNN可以获得更高的语义特征分级结果，平均准确率为89.37%。本文提出的语义特征的定量和自动分级可以帮助放射科医生有效地评估肺结节的恶性可能性，并进一步促进恶性结节的早期期待治疗。
Accurate grading of semantic characteristics is helpful for radiologists to determine the probabilities of the likelihood of malignancy of a pulmonary nodule. Nevertheless, because of the complex and varied properties of pulmonary nodules, assessing semantic characteristics (SC) is a difficult task. In this paper, we first analyze a set of important semantic characteristics of pulmonary nodules and extract the important SCs relating to pulmonary nodule malignancy by Pearson's correlation approach. Then, we propose three automatic SC grading models based on deep belief network (DBN) and a multi-branch convolutional neural network (CNN) classifier, MBCNN. The first DBN model takes grayscale and binary nodule images as the input, and the second DBN model takes grayscale nodule images and 72 features extracted from pulmonary nodules as the input. Experimental results indicate that our algorithms can achieve satisfying results on semantic characteristic grading. Especially, the MBCNN can obtain higher semantic characteristic grading results with an average accuracy of 89.37%. Quantitative and automatic grading of semantic characteristics proposed in this paper can assist radiologists effectively assess the likelihood of pulmonary nodules being malignant and further promote the early expectant treatment of malignant nodules.
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影响因子：
8.9
作者：
Huang, J;Ling, CX
通讯作者：
Ling, CX
影响因子：
4.4
作者：
Clark, Kenneth;Vendt, Bruce;Prior, Fred
通讯作者：
Prior, Fred
影响因子：
1.2
作者：
Okimoto, N;Asaoka, N;Fujita, K
通讯作者：
Fujita, K
DOI：
10.1109/tit.1962.1057692
发表时间：
1962-01-01
期刊：
IRE TRANSACTIONS ON INFORMATION THEORY
影响因子：
--
作者：
HU, M
通讯作者：
HU, M
影响因子：
1.9
作者：
Hodnett, Philip A.;Ko, Jane P.
通讯作者：
Ko, Jane P.