You have requested a machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Neither SPIE nor the owners and publishers of the content make, and they explicitly disclaim, any express or implied representations or warranties of any kind, including, without limitation, representations and warranties as to the functionality of the translation feature or the accuracy or completeness of the translations.
Translations are not retained in our system. Your use of this feature and the translations is subject to all use restrictions contained in the Terms and Conditions of Use of the SPIE website.
6 April 2005Comparison of three-class classification performance metrics: a case study in breast cancer CAD
Receiver Operating Characteristic (ROC) analysis is a widely used method for analyzing the performance of two-class classifiers. Advantages of ROC analysis include the fact that it explicitly considers the tradeoffs in sensitivity and specificity, includes visualization methods, and has clearly interpretable summary metrics. Currently, there does not exist a widely accepted performance method similar to ROC analysis for an N-class classifier (N>2). The purpose of this study was to empirically compare methods that have been proposed to evaluate the performance of N-class classifiers (N>2). These methods are, in one way or another, extensions of ROC analysis. This report focuses on three-class classification performance metrics, but most of the methods can be extended easily for more than three classes. The methods studied were pairwise ROC analysis, Hand and Till M Function (HTM), one-versus-all ROC analysis, a modified HTM, and Mossman's "Three-Way ROC" method. A three-class classification task from breast cancer computer-aided diagnosis (CADx) is taken as an example to illustrate the advantages and disadvantages of the alternative performance metrics.
Amit C. Patel andMia K. Markey
"Comparison of three-class classification performance metrics: a case study in breast cancer CAD", Proc. SPIE 5749, Medical Imaging 2005: Image Perception, Observer Performance, and Technology Assessment, (6 April 2005); https://doi.org/10.1117/12.595763
The alert did not successfully save. Please try again later.
Amit C. Patel, Mia K. Markey, "Comparison of three-class classification performance metrics: a case study in breast cancer CAD," Proc. SPIE 5749, Medical Imaging 2005: Image Perception, Observer Performance, and Technology Assessment, (6 April 2005); https://doi.org/10.1117/12.595763