Abstract
Spectrum analysis of ultrasonic echo signals has been showing potential for distinguishing cancerous from non-cancerous prostate tissues. Recently, using neural networks to classify tissue from spectrum analysis results has provided a powerful basis for imaging, guiding biopsies, and planning, executing, and monitoring therapy. ROC curves derived from leave-one-out evaluations of neural-network classifier performance have an area of 0.87/spl plusmn/0.04 compared to an area of 0.64/spl plusmn/0.04 for B-mode methods, which implies significantly superior differentiation of cancerous from non-cancerous prostate tissue.
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