Design of a mammography X-ray image classification assistant system adapted to Chinese population

Abstract

Objective‍ ‍To construct a mammography image classification assistant system suitable for Chinese population， and explore the potential of artificial intelligence technology to assist early screening of breast cancer in China. Methods‍ ‍Curated breast imaging subset of digital database for screening mammography （CBIS-DDSM）， Mammographic image analysis society database （MIAS） and other international open datasets were used to conduct model training respectively in order to reproduce the mainstream in-depth learning methods in the current literature. The model was also tested on the Chinese breast mammography database （CBMD） provided by Huajiao Technology Co.， Ltd， and the performance was compared. Aiming at the problem that the Chinese population data are not ideal in the performance test of the open dataset training model， an optimization strategy based on the sliding window adjustment mechanism was implemented in combination with the characteristics of Chinese population data. Then a two-stage migration learning method was designed to improve the overall performance of the model， and then development of our system was carried out. Results‍ ‍With the sliding window adjustment mechanism and the CBMD training model after two-stage transfer learning， the accuracy of our developed system was improved from 0.50 of the open datasets to 0.80， precision from 0.54 to 0.82， sensitivity from 0.52 to 0.80， F1 value from 0.52 to 0.80， and AUC value from 0.51 to 0.89 based on the Chinese population dataset as the test set. Conclusion‍ ‍Through the introduction of sliding window adjustment mechanism and two-stage migration learning strategy， the performance of the breast molybdenum target image classification model has been significantly improved in the Chinese population dataset， and our system primarily achieves the purpose of assisting the classification of breast molybdenum target images for the Chinese population.

Keywords

• mammography
• deep learning
• x-ray
• computer assistance