A topological approach for mammographic density classification using a modified synthetic minority over-sampling technique algorithm

Mammographic density is known to be a risk indicator for breast abnormalities development. Therefore, the breast tissue classification is an important part used in computer aided diagnosis (CAD) system to detect the cancer. In this paper, a CAD system for breast tissue classification using an equilibrating approach is proposed. The first contribution consists of using a representation of textons distribution by a topological map. This approach allows a good mammographic density classification using the distribution of breast tissue. The second contribution of this work consists of the equilibration of the dataset in the CAD system. Indeed, an improvement of the synthetic minority over-sampling technique (SMOTE) algorithm is developed. Our experiments are carried out with MIAS and digital database of screening mammography (DDSM) datasets to validate the CAD system and two different datasets to validate the proposed modified SMOTE algorithm. The obtained results confirm the validity of the presented proposal.