]> 2012-02-11T08:08:14-05:00 A Hybrid Classifier for Automated Radiologic Diagnosis: Preliminary Results and Clinical Applications We describe the design, implementation, and preliminary evaluation of a computer system to aid clinicians in the interpretation of cranial magnetic-resonance (MR) images. The system classifies normal and pathologic tissues in a test set of MR scans with high accuracy. It also provides a simple, rapid means whereby an unassisted expert may reliably label an image with his best judgment of its histologic composition, yielding a gold-standard image; this step facilitates objective evaluation of classifier performance. This system consists of a preprocessing module; a semiautomatic, reliable procedure for obtaining objective estimates of an expert's opinion of an image's tissue composition; a classification module based on a combination of the maximum-likelihood (ML) classifier and the ISODATA unsupervised-clustering algorithm;and an evaluation module based on confusion-matrix generation. The algorithms for classifier evaluation and gold-standard acquisition are advances over previous methods. Furthermore, the combination of a clustering algorithm and a statistical classifier provides advantages not found in systems using either method alone. techreport 1990 A Hybrid Classifier for Automated Radiologic Diagnosis: Preliminary Results and Clinical Applications KSL-90-46 1990 A Hybrid Classifier for Automated Radiologic Diagnosis: Preliminary Results and Clinical Applications 1990 Year Title YES Process note Number Ksl tr id Knowledge Systems, AI Laboratory Institution Has year Has where published Has title Has publishing details Has identifier Has author Bibtype Edward Herskovits Author Abstract KSL Technical Report Publication Technical Report