Content-based image retrieval (CBIR) technique is increasingly gaining research attention as a Computer Aided Diagnosis (CAD) approach for breast cancer diagnosis. This work discusses a novel feature modeling technique for CBIR systems based on classifier scores and standard statistical calculations on the same. Established textural and geometric features are initially used to represent medical characteristics, before being used to generate secondary features through classifier scoring using the Support Vector Machine and Quadratic Discriminant Analysis classifiers. The model is validated through a range of benchmarks, and is shown to perform competitively in comparison to similar works.
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