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Classification of Breast Tumors in Ultrasound Using Biclustering Mining by Samiha Selim is a document available to read on EtoBox.

This document presents a novel computer-aided diagnosis (CAD) system for classifying breast tumors in ultrasound images using a combination of biclustering mining and a back-propagation neural network. The proposed method achieved high accuracy, sensitivity, and specificity rates of 96.1%, 96.7%, and 95.7%, respectively, by utilizing BI-RADS features and a feature scoring scheme. The study emphasizes the effectiveness of the biclustering algorithm in discovering diagnostic patterns and improving the classif

Author

Samiha Selim

Language

EN