ArticleBioMed research international2016
Pulmonary Nodule Detection Model Based on SVM and CT Image Feature-Level Fusion with Rough Sets.
Article in BioMed research international, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Diagnostic efficiency of artificial intelligence for pulmonary nodules based on CT scans.American journal of translational research · 2023Article
- Multiclassifier fusion based on radiomics features for the prediction of benign and malignant primary pulmonary solid nodules.Annals of translational medicine · 2020Article
- Pulmonary Nodule Recognition Based on Multiple Kernel Learning Support Vector Machine-PSO.Computational and mathematical methods in medicine · 2018Article
- The application of artificial intelligence in EUS.Endoscopic ultrasoundReview
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4 authors.
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Abstract
In order to improve the detection accuracy of pulmonary nodules in CT image, considering two problems of pulmonary nodules detection model, including unreasonable feature structure and nontightness of feature representation, a pulmonary nodules detection algorithm is proposed based on SVM and CT image feature-level fusion with rough sets. Firstly, CT images of pulmonary nodule are analyzed, and 42-dimensional feature components are extracted, including six new 3-dimensional features proposed by this paper and others 2-dimensional and 3-dimensional features. Secondly, these features are reduced for five times with rough set based on feature-level fusion. Thirdly, a grid optimization model is used to optimize the kernel function of support vector machine (SVM), which is used as a classifier to identify pulmonary nodules. Finally, lung CT images of 70 patients with pulmonary nodules are collected as the original samples, which are used to verify the effectiveness and stability of the proposed model by four groups' comparative experiments. The experimental results show that the effectiveness and stability of the proposed model based on rough set feature-level fusion are improved in some degrees.
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