Proc. SPIE. 10134, Medical Imaging 2017: Computer-Aided Diagnosis
KEYWORDS: Breast, FDA class I medical device development, Computer aided diagnosis and therapy, Breast cancer, Computing systems, Feature extraction, Neural networks, System identification, Mammography, Image classification, Computer aided design, Prototyping
Mammography analysis is an important tool that helps detecting breast cancer at the very early stages of the disease, thus increasing the quality of life of hundreds of thousands of patients worldwide. In Computer-Aided Detection systems, the identification of mammograms with and without masses (without clinical findings) is highly needed to reduce the false positive rates regarding the automatic selection of regions of interest that may contain some suspicious content. In this work, the introduce a variant of the Optimum-Path Forest (OPF) classifier for breast mass identification, as well as we employed an ensemble-based approach that can enhance the effectiveness of individual classifiers aiming at dealing with the aforementioned purpose. The experimental results also comprise the naïve OPF and a traditional neural network, being the most accurate results obtained through the ensemble of classifiers, with an accuracy nearly to 86%.