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Cascade classifier shown to improve accuracy in mass vs. nonmass breast lesion differentiation

Researchers at the University of Toronto have tested and validated the superiority of a two-stage cascade classifier over a traditional, single-shot classifier when using computer-aided diagnosis (CAD) to differentiate between mass and nonmass breast lesions.
In a retrospective study of 280 histologically proved mass lesions and 129 histologically proved nonmass lesions identified in MR imaging studies, their cascaded classifier decreased the overall misclassification rate by 12 percent (72 of 409) of cases missed with cascade versus 82 of 409 missed with one-shot classifier.

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