ArticlePhysics in medicine and biology2019
Predicting lung nodule malignancies by combining deep convolutional neural network and handcrafted features.
Article in Physics in medicine and biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 2 of them syntheses that pooled it.
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Who cites it
25 citing papers in PubMed, 2 syntheses or guidelines pooled it, 79 citations in OpenAlex.
- A methodological framework for integrating generalisable deep learning and radiomics fusion model for early lung cancer detection across multi-centre imaging datasets.BMC medical informatics and decision making · 2026Pooled it
- Lung cancer risk prediction models based on pulmonary nodules: A systematic review.Thoracic cancer · 2022Pooled it
- Current trends and future directions of artificial intelligence in lung cancer diagnosis.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2026Article
- Integrated CT Radiomics and Circulating Tumor Cell Analysis in Predicting Lung Adenocarcinoma Invasion: A Dual-Center Study with Implications for Personalized Treatment.OncoTargets and therapy · 2026Article
- Lessons Learned From International Lung Cancer Screening Trials; People at Risk Deserve Screening for Early Detection.Respirology (Carlton, Vic.) · 2025Review
- MSA-Net: multiple self-attention mechanism for 3D lung nodule classification in CT images.BMC medical imaging · 2025Article
- Effective lung nodule detection using deep CNN with dual attention mechanisms.Scientific reports · 2024Article
- AI-Driven Models for Diagnosing and Predicting Outcomes in Lung Cancer: A Systematic Review and Meta-Analysis.Cancers · 2024Review
- Classification of Multiple H&E Images via an Ensemble Computational Scheme.Entropy (Basel, Switzerland) · 2023Article
- A novel fusion algorithm for benign-malignant lung nodule classification on CT images.BMC pulmonary medicine · 2023Article
- Performance of Deep-Learning Solutions on Lung Nodule Malignancy Classification: A Systematic Review.Life (Basel, Switzerland) · 2023Review
- Early Predicting Tribocorrosion Rate of Dental Implant Titanium Materials Using Random Forest Machine Learning Models.Tribology international · 2023Article
- Intratumoral and peritumoral CT-based radiomics strategy reveals distinct subtypes of non-small-cell lung cancer.Journal of cancer research and clinical oncology · 2022Article
- Lung Cancer Nodules Detection via an Adaptive Boosting Algorithm Based on Self-Normalized Multiview Convolutional Neural Network.Journal of oncology · 2022Article
- Radiomics in Lung Diseases Imaging: State-of-the-Art for Clinicians.Journal of personalized medicine · 2021Review
- Using a risk model for probability of cancer in pulmonary nodules.Thoracic cancer · 2021Article
- Lung Nodule Classification Using Biomarkers, Volumetric Radiomics, and 3D CNNs.Journal of digital imaging · 2021Article
- Multi-Dimension and Multi-Feature Hybrid Learning Network for Classifying the Sub Pathological Type of Lung Nodules through LDCT.Sensors (Basel, Switzerland) · 2021Article
- 3D CNN with Visual Insights for Early Detection of Lung Cancer Using Gradient-Weighted Class Activation.Journal of healthcare engineering · 2021Article
- On the robustness of deep learning-based lung-nodule classification for CT images with respect to image noise.Physics in medicine and biology · 2020Article
Corrections and comments
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Authors and funding
12 authors at 4 institutions in 2 countries.
Funding
Abstract
To predict lung nodule malignancy with a high sensitivity and specificity for low dose CT (LDCT) lung cancer screening, we propose a fusion algorithm that combines handcrafted features (HF) into the features learned at the output layer of a 3D deep convolutional neural network (CNN). First, we extracted twenty-nine HF, including nine intensity features, eight geometric features, and twelve texture features based on grey-level co-occurrence matrix (GLCM). We then trained 3D CNNs modified from three 2D CNN architectures (AlexNet, VGG-16 Net and Multi-crop Net) to extract the CNN features learned at the output layer. For each 3D CNN, the CNN features combined with the 29 HF were used as the input for the support vector machine (SVM) coupled with the sequential forward feature selection (SFS) method to select the optimal feature subset and construct the classifiers. The fusion algorithm takes full advantage of the HF and the highest level CNN features learned at the output layer. It can overcome the disadvantage of the HF that may not fully reflect the unique characteristics of a particular lesion by combining the intrinsic CNN features. Meanwhile, it also alleviates the requirement of a large scale annotated dataset for the CNNs based on the complementary of HF. The patient cohort includes 431 malignant nodules and 795 benign nodules extracted from the LIDC/IDRI database. For each investigated CNN architecture, the proposed fusion algorithm achieved the highest AUC, accuracy, sensitivity, and specificity scores among all competitive classification models.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.