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Classification methods from statistical pattern recognition, neural nets, and machine learning were applied to four real-world data sets. Each of these data sets has been previously analyzed and reported in the statistical, medical, or machine learning literature. The data sets are characterized by statisucal uncertainty; there is no completely accurate(More)
Axial and sagittal magnetic resonance (MR) sections and contiguous sections of axial positron emission tomographic (PET) images obtained with fludeoxyglucose F-18 were used to evaluate a new method of registering three-dimensional images of the brain. The users specified the interhemispheric fissure plane in three dimensions for both the MR and PET data(More)
  • I Kapouleas
  • 1990
A new approach to automating radiologic diagnosis is described and tested in a system that locates multiple sclerosis lesions in magnetic resonance human brain images. This approach uses a step-by-step procedure, where the most obvious features in the images are identified first, and used to calibrate the application of the next step, until the desired(More)
In this paper, twenty well known data mining classification methods are applied on ten UCI machine learning medical datasets and the performance of various classification methods are empirically compared while varying the number of categorical and numeric attributes, the types of attributes and the number of instances in datasets. In the performance study,(More)
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