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Volumn 1281, Issue , 2005, Pages 1109-1114

False positive reduction for lung nodule CAD using support vector machines and genetic algorithms

Author keywords

Classification; Computer aided detection; Computer aided diagnosis; CT; False positive reduction; Feature extraction; Feature selection; Genetic algorithm; Lung nodule; Machine learning; Multi slice CT; Support vector machine

Indexed keywords


EID: 33646457262     PISSN: 05315131     EISSN: None     Source Type: Book Series    
DOI: 10.1016/j.ics.2005.03.061     Document Type: Article
Times cited : (16)

References (12)
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  • 3
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  • 4
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    • Automatic detection of lung nodules from multi-slice low-dose CT images
    • Fan L., et al. Automatic detection of lung nodules from multi-slice low-dose CT images. SPIE Medical Imaging (2001) 1828-1835
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    • Fan, L.1
  • 5
    • 0028537379 scopus 로고
    • Reduction of false positives in computerized detection of lung nodules in chest radiographs using artificial neural networks, discriminant analysis, and a rule-based scheme
    • Wu Y.C., et al. Reduction of false positives in computerized detection of lung nodules in chest radiographs using artificial neural networks, discriminant analysis, and a rule-based scheme. Journal of Digital Imaging 7 4 (1994) 196-207
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  • 7
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    • Massive training artificial neural network (MTANN) for reduction of false positives in computerized detection of lung nodules in low-dose computed tomography
    • Suzuki K., et al. Massive training artificial neural network (MTANN) for reduction of false positives in computerized detection of lung nodules in low-dose computed tomography. Med. Phys. 30 7 (2003) 1602-1617
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  • 12
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    • The CHC adaptive search algorithm: how to have safe search when engaging in nontraditional genetic recombination
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.