Fusing radiomics and deep learning features for automated classification of multi‐type pulmonary nodule

The fusion of radiomics features and deep learning neural network features can effectively enhance the overall performance of pulmonary nodule classification models while also improving the recognition of minority classes when there is a significant class imbalance.

Authors

Yue Che (ORCID: https://orcid.org/0009-0004-4634-2417)
Guozhi Tang (ORCID: https://orcid.org/0000-0003-0859-5195)
Xin Chen (ORCID: https://orcid.org/0000-0003-1950-2468)
Lingyan Du (ORCID: https://orcid.org/0000-0002-1743-0539)
S.-F. Ling (ORCID: https://orcid.org/0009-0006-5520-6665)
Xing-Liang Pan

Institutions

Zigong First People's Hospital (CN)
Sichuan University of Science and Engineering (CN)

Publication Details

Journal: Medical Physics
Published: 2025-05-20
DOI: https://doi.org/10.1002/mp.17901
Citations: 1
Primary Topic: Radiomics and Machine Learning in Medical Imaging
Type: article
Field-Weighted Citation Impact: 1.36

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article

Fusing radiomics and deep learning features for automated classification of multi‐type pulmonary nodule

Yue Che, Guozhi Tang, Xin Chen, Lingyan Du et al.

May 20, 2025

1 citations

Medical Physics

Radiomics and Machine Learning in Medical Imaging

1.36

article

Fusing radiomics and deep learning features for automated classification of multi‐type pulmonary nodule

Yue Che, Guozhi Tang, Xin Chen, Lingyan Du, S.-F. Ling, Xing-Liang Pan

May 20, 2025article en

1 citations

Abstract

The fusion of radiomics features and deep learning neural network features can effectively enhance the overall performance of pulmonary nodule classification models while also improving the recognition of minority classes when there is a significant class imbalance.

Medical PhysicsVol. 52(7)

Zigong First People's Hospital (CN), Sichuan University of Science and Engineering (CN)

Reduced inequalities

Openalex Percentile: Top 20%

Radiomics and Machine Learning in Medical Imaging

1.36

DOI Source OpenAlex

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