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A comparative study of machine learning methods for lung diseases diagnosis by computerized digital imaging'"
DC Field | Value | Language |
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dc.contributor.author | Kang, Suk Ho | - |
dc.contributor.author | Lee, Youngjoo | - |
dc.date.accessioned | 2010-01-13T06:42:58Z | - |
dc.date.available | 2010-01-13T06:42:58Z | - |
dc.date.issued | 2007-08 | - |
dc.identifier.citation | Journal of information and operations management, Vol.17 No.1, pp. 1-9 | - |
dc.identifier.uri | https://hdl.handle.net/10371/29925 | - |
dc.description.abstract | In this study. we tested and comparcd several state-of-art machine-learning
niethods for automated classification of obstructive lung diseases based on the features from text,ure analysis using HRCT (High 1Zesolution Computerized Tomography) images. I-IRCT can provide accurate images for the detection of various obstructive lung diseases, including centrilobular emphysema, panlobuclar emphysema and constrictive bronchiolitis. Features on the HHCT images. however, can be subtle, particularly in the early stages of disease. and image based diagnosis is subject to jnter-observer variation. In order to support the clinical diagnosis and improve its accuracy. three different types of automated classification systems were developed and comparcd based on the classification performance and clinical applicability. Not only Bayesian classifier, a typical kind of statistic method, but also ANX (Artificial Neural Network) and SVM (Support Vector Machine) were employed. We tested these three classifiers for the differentiation of normal and three types of obstructive lung diseases. The ANN showed the best performance of 86.0% overall sensitivity and there is significant difference among other classifiers (one-way ANOVA, ~ ( 0 . 0 1 )I.n discussion, we addressed what characteristic of each classifier made differences in the performance and which classifier was more suitable for clinical applications and proposed appropriate way to choose the best classifier and determine its optimal parameters to discriminate the diseases better. This result can be applied to the classifier for differentiation of other diseases. | - |
dc.language.iso | en | - |
dc.publisher | 서울대학교 경영정보연구소 | - |
dc.title | A comparative study of machine learning methods for lung diseases diagnosis by computerized digital imaging'" | - |
dc.type | SNU Journal | - |
dc.contributor.AlternativeAuthor | 강석호 | - |
dc.contributor.AlternativeAuthor | 이영주 | - |
dc.citation.journaltitle | Journal of information and operations management(경영정보논총) | - |
dc.citation.endpage | 9 | - |
dc.citation.number | 1 | - |
dc.citation.pages | 1-9 | - |
dc.citation.startpage | 1 | - |
dc.citation.volume | 17 | - |
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