Low-Scale Morphological Feature Recalibration Method for Breast Ultrasound Classification
Breast ultrasound images have large intra-class differences,small inter-class differences,and complex and variable nodule shapes.In order to address these issues,a breast ultrasound image algorithm with morphological feature recalibration was designed to realize automatic diagnosis of breast ultrasound.First,an end-to-end network model was built,which adopted progressive training to fully learn the more discriminative regions in the image and obtain more fine-grained feature information.Secondly,a partition shuffle mechanism was proposed to reduce the noise caused by the disruption of the nodule region when the image was shuffled.Then,the features extracted from the bottom layer of the model were recalibrated with the morphological features obtained through the mask image,and a low-scale recalibration loss function was proposed.Finally,in order to verify the effectiveness of the proposed method,a low-scale recalibration database(LSRD)containing 1 550 breast ultrasound images was constructed.The experimental results show that the accuracy of the proposed model on LSRD is 94.3％;the sensitivity is 91.2％;the specificity is 93.6％,and the area(AUC)under the receiver operator characteristic curve(ROC)is 0.941,all of which are superior to other comparison models.On the breast ultrasound image(BUSI)dataset,compared with the other models,the classification accuracy of the proposed model is improved by 3.3％.
morphological featurespartition shuffle mechanismlow-scale recalibrationbreast cancer classification
万方数据
维普
