Abstract: Breast cancer is one of the most common malignant tumors of women.The incidence of breast cancer is increasing year by year,which seriously threatens the health of the patients.In recent years,more and more attention has been paid to how to replace the traditional needle biopsy in the diagnosis of benign and malignant breast nodules.Medical research shows that significant differences exist on the edge of benign and malignant nodules.So the algorithm of boundary enhancement treatment provides a new way for the study of judgment of benign and malignant breast cancer.The database was constructed with the support of Beijing Friendship Hospital which is affiliated to Capital Medical University.The images are expanded based on the comparison of 5 kinds of boundary enhancement (ACE) algorithm.AlexNet network model is used which is excellent in image classification.The data processed by linear,nonlinear contrast stretching,histogram equalization,histogram thresholding and adaptive contrast enhancement algorithm are applied to the AlexNet model.The influence of the five algorithms on the accuracy of AlexNet model is compared,and a preprocessing algorithm,which is more suitable for ultrasonic images of breast nodules,is obtained.The total number of images in the expanded data set is more than ten thousand,of which the training set is 80%,and the verification set and the test set account for 10% each.Finally,the sensitivity,specificity and accuracy parameters are calculated by plotting the ROC curve,and the test results are evaluated.The better test results are obtained.

Key words: AlexNet model, Automatic contrast enhancement algorithm, Breast cancer, Breast nodules, Convolutional neutral network, Deep learning, Image preprocessing