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This paper presents a classification-driven biomedical image retrieval approach based on multi-class support vector machine (SVM) and uses image filtering and similarity fusion. In this framework, the probabilistic outputs of the SVM are exploited to reduce the search space for similarity matching. In addition, the predicted category of the query image is used for linear combination of similarity. The method is evaluated on a diverse collection of 5000 biomedical images of different modalities, body parts, and orientations and shows a halving in computation time (efficiency) and 10% to 15% improvement in precision at each recall level (effectiveness).