Evidence map›Paper›PMID 40304352›Full record

ArticleCurrent medical imaging2025

A Novel Automatic Lung Nodule Classification Scheme using Fusion Ghost Convolution and Hybrid Normalization in Chest CTs

Yu Gu, Nan Wang, Jiaqi Liu, Lidong Yang, Baohua Zhang, Jing Wang, Xiaoqi Lu, Jianjun Li, XIn Liu, Siyuan Tang and 1 more

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Article in Current medical imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

11 authors.

Yu GuInner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Digital and Intelligent Industry, Inner Mongolia University of Science and Technology, Baotou 014010, China.ORCID 0000-0003-4345-6932
Nan WangInner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Digital and Intelligent Industry, Inner Mongolia University of Science and Technology, Baotou 014010, China.ORCID 0009-0003-7036-6817
Jiaqi LiuInner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Digital and Intelligent Industry, Inner Mongolia University of Science and Technology, Baotou 014010, China.
Lidong YangInner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Digital and Intelligent Industry, Inner Mongolia University of Science and Technology, Baotou 014010, China.
Baohua ZhangSchool of Automation and Electrical Engineering, Inner Mongolia University of Science and Technology, Baotou, 014010, China.
Jing WangSchool of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China.
Xiaoqi LuInner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Digital and Intelligent Industry, Inner Mongolia University of Science and Technology, Baotou 014010, China.
Jianjun LiInner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Digital and Intelligent Industry, Inner Mongolia University of Science and Technology, Baotou 014010, China.
XIn LiuInner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Digital and Intelligent Industry, Inner Mongolia University of Science and Technology, Baotou 014010, China.
Siyuan TangInner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Digital and Intelligent Industry, Inner Mongolia University of Science and Technology, Baotou 014010, China.
Qun HeInner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Digital and Intelligent Industry, Inner Mongolia University of Science and Technology, Baotou 014010, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo address the low efficiency of diagnosing pulmonary nodules using computed tomography (CT) images and the difficulty in obtaining the key signs of malignant pulmonary nodules, a ghost convolution residual network incorporating hybrid normalization (GCHN-net) is proposed.

methodsFirstly, a three-dimensional ghost convolution with a small kernel is embedded in the GCHN-net. Secondly, we designed a hybrid normalizedactivation module (TMNAM) that can handle the rich and complex features of lung nodules in both the deep and shallow layers of the network, and incorporating two different normalization methods. This allows the network to comprehensively learn the intricate relationships underlying the intrinsic features of lung nodules and enhances its capacity to classify the properties of unknown nodules. Additionally, to enhance the accuracy and detail of the category activation map, GradCAM++ is integrated into the third layer of the GCHN-net. This integration enables the visualization of specific regions within three-dimensional lung nodules that the model focuses on during its predictions.

resultsThe accuracy of the GCHN-net on the Lung Nodule Analysis 16 (LUNA16) dataset was 90.22%, with an F1-score of 88.31% and a G-mean of 90.48%.

conclusionCompared with existing methods, the proposed method can greatly improve the classification of pulmonary nodules and can effectively assist doctors in diagnosing patients with pulmonary nodules.

Indexed as

Lung NeoplasmsMultiple Pulmonary NodulesRadiographic Image Interpretation, Computer-AssistedSolitary Pulmonary NoduleTomography, X-Ray ComputedHumansImaging, Three-DimensionalClassification of pulmonary nodulesComputed tomographyGhost convolutionLung Nodule Analysis 16.NormalizationVisualization

Identifiers

PMID40304352
PMCPMC13181202

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