Positioned Quality Evaluation of Posterior Breast by Consensus Decision-making from Multi-candidate Classification Results

This paper presents a positioned quality evaluation method for posterior breasts in Craniocaudal-view mammograms. The proposed method employs a convolutional neural architecture (CNN) to recognize multiple candidates extracted from the posterior-inner and posterior-outer breasts and determines whether visualize fat posterior to glandular tissues. To find the proper classifier, we experiment with the two-category classifications with various CNN models, including, Squeezenet, Densenet-121, Alexnet, Vgg-19, and Resnet-101. Our method determines the evaluation result of posterior breasts by consensus decision-making from the classification results of multiple candidates. The experiment results demonstrate that Densenet-121 is the best choice for this work.