ArticleJournal of digital imaging2010
Feature selection and performance evaluation of support vector machine (SVM)-based classifier for differentiating benign and malignant pulmonary nodules by computed tomography.
Article in Journal of digital imaging, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
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Who cites it
28 citing papers in PubMed, 1 synthesis or guideline pooled it, 95 citations in OpenAlex.
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- Combining machine learning and texture analysis to differentiate mediastinal lymph nodes in lung cancer patients.Physical and engineering sciences in medicine · 2021Article
- Three-Dimensional Texture Feature Analysis of Pulmonary Nodules in CT Images: Lung Cancer Predictive Models Based on Support Vector Machine Classifier.Journal of digital imaging · 2020Article
- Post-imaging pulmonary nodule mathematical prediction models: are they clinically relevant?European radiology · 2019Observational
- Machine learning approach for distinguishing malignant and benign lung nodules utilizing standardized perinodular parenchymal features from CT.Medical physics · 2019Article
- Feature Selection for Automatic Tuberculosis Screening in Frontal Chest Radiographs.Journal of medical systems · 2018Article
- MRI features predict p53 status in lower-grade gliomas via a machine-learning approach.NeuroImage. Clinical · 2018Article
- Correlation coefficient based supervised locally linear embedding for pulmonary nodule recognition.Computer methods and programs in biomedicine · 2016Article
- LUNGx Challenge for computerized lung nodule classification.Journal of medical imaging (Bellingham, Wash.) · 2016Article
- Improving malignancy prediction through feature selection informed by nodule size ranges in NLST.Conference proceedings. IEEE International Conference on Systems, Man, and Cybernetics · 2016Article
- Automated system for lung nodules classification based on wavelet feature descriptor and support vector machine.Biomedical engineering online · 2015Article
- Test-retest reproducibility analysis of lung CT image features.Journal of digital imaging · 2014Article
- The development and evaluation of a computerized diagnosis scheme for pneumoconiosis on digital chest radiographs.Biomedical engineering online · 2014Article
- Support vector machine model for diagnosing pneumoconiosis based on wavelet texture features of digital chest radiographs.Journal of digital imaging · 2014Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
There are lots of work being done to develop computer-assisted diagnosis and detection (CAD) technologies and systems to improve the diagnostic quality for pulmonary nodules. Another way to improve accuracy of diagnosis on new images is to recall or find images with similar features from archived historical images which already have confirmed diagnostic results, and the content-based image retrieval (CBIR) technology has been proposed for this purpose. In this paper, we present a method to find and select texture features of solitary pulmonary nodules (SPNs) detected by computed tomography (CT) and evaluate the performance of support vector machine (SVM)-based classifiers in differentiating benign from malignant SPNs. Seventy-seven biopsy-confirmed CT cases of SPNs were included in this study. A total of 67 features were extracted by a feature extraction procedure, and around 25 features were finally selected after 300 genetic generations. We constructed the SVM-based classifier with the selected features and evaluated the performance of the classifier by comparing the classification results of the SVM-based classifier with six senior radiologists' observations. The evaluation results not only showed that most of the selected features are characteristics frequently considered by radiologists and used in CAD analyses previously reported in classifying SPNs, but also indicated that some newly found features have important contribution in differentiating benign from malignant SPNs in SVM-based feature space. The results of this research can be used to build the highly efficient feature index of a CBIR system for CT images with pulmonary nodules.
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