ArticleMedical & biological engineering & computing2020
Automated lung cancer diagnosis using three-dimensional convolutional neural networks.
Article in Medical & biological engineering & computing, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Lung cancer detection in CT scans using extracted nodule features and precise multistage segmentation with deep learning based ensemble model.Scientific reports · 2026Article
- Role of Three-Dimensional Convolution Neural Networks (3D-CNN) in Image Processing and Recognition in Oncology: A Systematic Review and Meta-Analysis.International journal of hematology-oncology and stem cell research · 2026Review
- Optimizing non small cell lung cancer detection with convolutional neural networks and differential augmentation.Scientific reports · 2025Article
- XLLC-Net: A lightweight and explainable CNN for accurate lung cancer classification using histopathological images.PloS one · 2025Article
- MSTS-Net: malignancy evolution prediction of pulmonary nodules from longitudinal CT images via multi-task spatial-temporal self-attention network.International journal of computer assisted radiology and surgery · 2023Article
- Medical Images Segmentation for Lung Cancer Diagnosis Based on Deep Learning Architectures.Diagnostics (Basel, Switzerland) · 2023Article
- Identification and Classification of Lungs Focal Opacity Using CNN Segmentation and Optimal Feature Selection.Computational intelligence and neuroscience · 2023Article
- Deep Learning Techniques to Diagnose Lung Cancer.Cancers · 2022Review
- An Efficient Multi-Scale Convolutional Neural Network Based Multi-Class Brain MRI Classification for SaMD.Tomography (Ann Arbor, Mich.) · 2022Article
- Lung Nodule Malignancy Prediction in Sequential CT Scans: Summary of ISBI 2018 Challenge.IEEE transactions on medical imaging · 2021Article
- State-of-the-Art Challenges and Perspectives in Multi-Organ Cancer Diagnosis via Deep Learning-Based Methods.Cancers · 2021Review
- Screen-detected solid nodules: from detection of nodule to structured reporting.Translational lung cancer research · 2021Review
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Lung cancer is the deadliest cancer worldwide. It has been shown that early detection using low-dose computer tomography (LDCT) scans can reduce deaths caused by this disease. We present a general framework for the detection of lung cancer in chest LDCT images. Our method consists of a nodule detector trained on the LIDC-IDRI dataset followed by a cancer predictor trained on the Kaggle DSB 2017 dataset and evaluated on the IEEE International Symposium on Biomedical Imaging (ISBI) 2018 Lung Nodule Malignancy Prediction test set. Our candidate extraction approach is effective to produce accurate candidates with a recall of 99.6%. In addition, our false positive reduction stage classifies successfully the candidates and increases precision by a factor of 2000. Our cancer predictor obtained a ROC AUC of 0.913 and was ranked 1st place at the ISBI 2018 Lung Nodule Malignancy Prediction challenge. Graphical abstract.
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