A computer-aided system for classification of breast tumors in ultrasound images via biclustering learning

The occurance of Breast cancer increases significantly in the modern world. Therefore, the importance of computer-aided recognition of breast tumors also increases in clinical diagnosis. This paper proposes a novel computer-aided diagnosis (CAD) method for the classification of breast lesions as benign or malignant tumors using the biclustering learning technique. The medical data is graded based on the sonographic breast imaging reporting with data system (BI-RADS) lexicon. In the biclustering learning, the training data is used to find significant grading patterns. The grading pattern being learned is then applied to the test data. The k-Nearest Neighbors (k-NN) classifier is used as the classifier of breast tumors. Experimental results demonstrate that the proposed method classifies breast tumors into benign and malignant effectively. This indicates that it could yield good performances in real applications.