Classification and diagnosis of ultrasound images with breast tumors based on transfer learning / 中国医学影像技术

ABSTRACT

Objective: To investigate the value of transfer learning methods in classification of ultrasound images of benign and malignant breast tumors. Methods Ultrasonic features of histopathologically proved breast tumors in 447 patients were retrospectively analyzed. The features of original images were extracted using the method of principal component analysis. Matlab 7.0 software was used for achieving transfer learning method. Finally, the quantitative image characteristics were inputted into the program in order to use new methods of transfer learning for identifying the benign and malignant breast tumors. Results The quantitative parameters of ultrasound images with malignant breast tumors, such as edge roughness, firmness, neighborhood gray-tone difference matrix roughness, echo difference between the posterior and peripheral areas of the masses, and the horizontal high-frequency and vertical low-frequency components-histogram energy were significantly higher than those of the benign breast tumors (all P<0.05). The sensitivity, specificity, the accuracy of the ultrasound and transfer learning method in diagnosis of malignant breast tumors was 96.21% (127/132) and 96.04% (97/101), 66.35% (209/315) and 98.49% (196/199), 75.17% (336/447) and 97.67% (293/300), respectively. Conclusion Quantitative ultrasonic features can provide objective quantitative parameters for identification of benign and malignant breast tumors. Transfer learning methods can effectively classify ultrasound images with benign and malignant breast tumors.