Deep Cross-Training: An Approach to Improve Deep Neural Network Classification on Mammographic Images

Breast cancer is the leading cause of death among women. In Brazil, the estimated risk corresponds to 61.61 new cases per 100,000 women per year. The strategy of screening for the disease through mammography favors the early detection of the illness. However, the identification of lesions in mammographic images is a complex process, with technical and biological limitations. To deal with that, deep learning techniques have been employed in Computer-Aided Diagnosis Systems. This work presents an approach for improving the accuracy of cancer screening by applying deep learning: Deep Cross-Training. The approach was evaluated by classifying mammographic images from the INbreast and CBIS-DDSM databases, developing binary and multiclass models based on the ResNet-18 architecture. Through the experiments, it was possible to verify that the DCTr approach increased the classification accuracy and precision by 39% and 36%, respectively. Confirming an average precision of 0.760 +/- 0.188 in predicting the ROIs of mammograms.