Convolutional Neural Networks Based Classification of Mammograms

Abstract

This paper presents the application of convolutional neural networks (CNNs) for developing a classification system of X-Ray images into three classes: Normal, Benign, and Malignant. For training convolutional neural network models, we use nearly 9000 Vietnamese mammograms, which were collected and annotated by the radiologists of the Vietnam National Cancer Hospital. Our experiments were conducted with the 2 phases with two different data sets. The first phase experimented with multiple training options, as with the weighted loss function, the gray-level pre-processing, and the same number of samples of classes. The results of experiments in the first phase would form the basis for the second phase for developing a mammogram classification system based on the CNN with ResNet 18. The evaluation of system performance using the testing set achieves a macAUC of 0.828247, an average sensitivity of 0.64738, and an average specificity of 0.825670. In particular, the system accuracy achieves an AUC of 0.894291 in predicting the presence of cancer. The evaluation of system performance achieved a macAUC of 0.828247 which is higher than a macAUC of 0.754 for the classifying images into BI-RADS 045, BI-RADS 1, BI-RADS 23 categories when using 7912 breast cancer X-Ray images collected by Vietnam Hanoi Medical University Hospital for ResNet 50 training.