ArticleLa Radiologia medica2020
Classification of lung nodules based on CT images using squeeze-and-excitation network and aggregated residual transformations.
Article in La Radiologia medica, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
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Who cites it
28 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Lung Cancer Imaging: Screening Result and Nodule Management.International journal of environmental research and public health · 2022Pooled it
- LNMSNet: a multi-task deep learning network for pulmonary nodules segmentation and malignancy classification.Frontiers in medicine · 2026Article
- MSA-Net: multiple self-attention mechanism for 3D lung nodule classification in CT images.BMC medical imaging · 2025Article
- Enhanced microvascular imaging through deep learning-driven OCTA reconstruction with squeeze-and-excitation block integration.Biomedical optics express · 2024Article
- Anti-HER2 therapy response assessment for guiding treatment (de-)escalation in early HER2-positive breast cancer using a novel deep learning radiomics model.European radiology · 2024Article
- Pulmonary Nodule Classification Using a Multiview Residual Selective Kernel Network.Journal of imaging informatics in medicine · 2024Article
- PB-LNet: a model for predicting pathological subtypes of pulmonary nodules on CT images.BMC cancer · 2023Article
- Spatial attention-based residual network for human burn identification and classification.Scientific reports · 2023Article
- An automatic classification of pulmonary nodules for lung cancer diagnosis using novel LLXcepNN classifier.Journal of cancer research and clinical oncology · 2023Article
- Single Modality vs. Multimodality: What Works Best for Lung Cancer Screening?Sensors (Basel, Switzerland) · 2023Article
- Label correlation transformer for automated chest X-ray diagnosis with reliable interpretability.La Radiologia medica · 2023Article
- The predictive value of chest computed tomography images, tumor markers, and metabolomics in the identification of benign and malignant pulmonary nodules.Journal of thoracic disease · 2023Article
- Application of radiomics in diagnosis and treatment of lung cancer.Frontiers in pharmacology · 2023Review
- A proposed methodology for detecting the malignant potential of pulmonary nodules in sarcoma using computed tomographic imaging and artificial intelligence-based models.Frontiers in oncology · 2023Article
- Preliminary Experience of Liquid Biopsy in Lung Cancer Compared to Conventional Assessment: Light and Shadows.Journal of personalized medicine · 2022Article
- Heart Failure and Cardiomyopathies: CT and MR from Basics to Advanced Imaging.Diagnostics (Basel, Switzerland) · 2022Review
- Ada-GridRF: A Fast and Automated Adaptive Boost Based Grid Search Optimized Random Forest Ensemble model for Lung Cancer Detection.Physical and engineering sciences in medicine · 2022Article
- Diffusion weighted imaging and diffusion kurtosis imaging in abdominal oncological setting: why and when.Infectious agents and cancer · 2022Review
- Imaging Features of Post Main Hepatectomy Complications: The Radiologist Challenging.Diagnostics (Basel, Switzerland) · 2022Review
- Complications Risk Assessment and Imaging Findings of Thermal Ablation Treatment in Liver Cancers: What the Radiologist Should Expect.Journal of clinical medicine · 2022Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Lung cancer is pointed as a leading cause of cancer death worldwide. Early lung nodule diagnosis has great significance for treating lung cancer and increasing patient survival. In this paper, we present a novel method to classify the malignant from benign lung nodules based on CT images using squeeze-and-excitation network and aggregated residual transformations (SE-ResNeXt). The state-of-the-art SE-ResNeXt module, which integrates the advantages of SENet for feature recalibration and ResNeXt for feature reuse, has great ability in boosting feature discriminability on imaging pattern recognition. The method is evaluated on the public available LUng Nodule Analysis 2016 (LUNA16) database with 1004 (450 malignant and 554 benign) nodules, achieving an area under the receiver operating characteristic curve (AUC) of 0. 9563 and accuracy of 91.67%. The promising results demonstrate that our method has strong robustness in the classification of nodules. The method has the potential to help radiologists better interpret diagnostic data and differentiate the benign from malignant lung nodules on CT images in clinical practice. To our best knowledge, the effectiveness of SE-ResNeXt on lung nodule classification has not been extensively explored.
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Registered trials
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