ArticleBioMed research international2020
Spiculation Sign Recognition in a Pulmonary Nodule Based on Spiking Neural P Systems.
Article in BioMed research international, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Mechanisms of osteopontin-stabilized amorphous calcium phosphate calcification in benign and pre-malignant breast disease.Scientific reports · 2025Article
- Efficacy of exponentiation method with a convolutional neural network for classifying lung nodules on CT images by malignancy level.European radiology · 2023Article
- Factors associated with overdiagnosis of benign pulmonary nodules as malignancy: a retrospective cohort study.BMC pulmonary medicine · 2023Article
- An uncertainty-aware self-training framework with consistency regularization for the multilabel classification of common computed tomography signs in lung nodules.Quantitative imaging in medicine and surgery · 2023Article
- Classification and Segmentation Algorithm in Benign and Malignant Pulmonary Nodules under Different CT Reconstruction.Computational and mathematical methods in medicine · 2022Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The spiculation sign is one of the main signs to distinguish benign and malignant pulmonary nodules. In order to effectively extract the image feature of a pulmonary nodule for the spiculation sign distinguishment, a new spiculation sign recognition model is proposed based on the doctors' diagnosis process of pulmonary nodules. A maximum density projection model is established to fuse the local three-dimensional information into the two-dimensional image. The complete boundary of a pulmonary nodule is extracted by the improved Snake model, which can take full advantage of the parallel calculation of the Spike Neural P Systems to build a new neural network structure. In this paper, our experiments show that the proposed algorithm can accurately extract the boundary of a pulmonary nodule and effectively improve the recognition rate of the spiculation sign.
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