ArticleJournal of X-ray science and technology2019
Expert knowledge-infused deep learning for automatic lung nodule detection.
Article in Journal of X-ray science and technology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Multi-scale detection of pulmonary nodules by integrating attention mechanism.Scientific reports · 2023Article
- Ki-Cook: clustering multimodal cooking representations through knowledge-infused learning.Frontiers in big data · 2023Article
- Deep Learning Techniques to Diagnose Lung Cancer.Cancers · 2022Review
- Deep Learning Application for Analyzing of Constituents and Their Correlations in the Interpretations of Medical Images.Diagnostics (Basel, Switzerland) · 2021Review
- Deep convolutional neural networks for multiplanar lung nodule detection: Improvement in small nodule identification.Medical physics · 2021Article
- Artificial Intelligence-Aided Diagnosis Software to Identify Highly Suspicious Pulmonary Nodules.Frontiers in oncology · 2021Article
- 3D-GLCM CNN: A 3-Dimensional Gray-Level Co-Occurrence Matrix-Based CNN Model for Polyp Classification via CT Colonography.IEEE transactions on medical imaging · 2020Article
- Energy enhanced tissue texture in spectral computed tomography for lesion classification.Visual computing for industry, biomedicine, and art · 2019Article
- Improved computer-aided detection of pulmonary nodules via deep learning in the sinogram domain.Visual computing for industry, biomedicine, and art · 2019Article
- Developing global image feature analysis models to predict cancer risk and prognosis.Visual computing for industry, biomedicine, and art · 2019Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
backgroundComputer aided detection (CADe) of pulmonary nodules from computed tomography (CT) is crucial for early diagnosis of lung cancer. Self-learned features obtained by training datasets via deep learning have facilitated CADe of the nodules. However, the complexity of CT lung images renders a challenge of extracting effective features by self-learning only. This condition is exacerbated for limited size of datasets. On the other hand, the engineered features have been widely studied.
objectiveWe proposed a novel nodule CADe which aims to relieve the challenge by the use of available engineered features to prevent convolution neural networks (CNN) from overfitting under dataset limitation and reduce the running-time complexity of self-learning.
methodsThe CADe methodology infuses adequately the engineered features, particularly texture features, into the deep learning process.
resultsThe methodology was validated on 208 patients with at least one juxta-pleural nodule from the public LIDC-IDRI database. Results demonstrated that the methodology achieves a sensitivity of 88% with 1.9 false positives per scan and a sensitivity of 94.01% with 4.01 false positives per scan.
conclusionsThe methodology shows high performance compared with the state-of-the-art results, in terms of accuracy and efficiency, from both existing CNN-based approaches and engineered feature-based classifications.
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