[The application of artificial intelligence on the classification of benign and malignant breast tumors based on...

This retrospective analysis was conducted on clinical obtained DCE-MR images of 198 patients, age from 21 to 79 years(45.5±13.7). The CBAM-ResNet model was developed to perform the classification automatically at the image-level based on deep learning method using the pathological examination as the reference standard,then the classification result of each individual patient was obtained by ensemble learning. The proposed method can have an accuracy of 82.69% for correctly distinguishing between benign and malignant breast tumors at the slice-level based on CBAM-ResNet model and with a sensitivity of 85.67%.. After the voting mechanism is applied, the classification accuracy can reach up to 88.24% at the patient-level with a sensitivity of 87.50%. Our experimental results demonstrated the proposed approach have a high classification accuracy.