Bi-modal breast cancer classification system
Breast cancer is known as one of the major causes of mortality among women. Breast cancer can be treated with better patient outcomes and significantly lower costs if it is detected early. Digital mammograms are the type of medical images most often used, and which are the most reliable, for the detection of breast cancer. The presence of microcalcification clusters in mammograms contributes to evidence for the detection of early stages of cancer. In this paper, a bi-modal artificial neural network (ANN) based breast cancer classification system is proposed. The microcalcifications are extracted with adaptive neural networks that are trained with cancer/malignant and normal/benign breast digital mammograms of both cranio caudal (CC) and medio-latral oblique (MLO) views. The performance of the networks is evaluated using receiver operating characteristic (ROC) curve analysis. Sensitivityspecificity of 98.0100.0 for the CC view and 96.0100.0 for the MLO view networks are recorded for 200 unseen digital database for screening mammography (DDSM) cases. The DDSM database, developed at the University of South Florida, is a resource for use by the mammographic image analysis research community. The OR logic is then used to fuse individual networks to get a best sensitivityspecificity of 100.0100.0 for the ensemble. However, the overall sensitivityspecificity of the ANN ensemble is somewhat degraded at the expense of a robust or sensitive system, i.e., the probability to miss out a true positive case is minimized.
breast cancer classification digital mammogram medical imaging tumor pattern classification combined classifier computer vision