Design of a mammography X-ray image classification assistant system adapted to Chinese population
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...
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Editorial Office of Journal of Army Medical University
2025-01-01
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| Series: | 陆军军医大学学报 |
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| Online Access: | https://aammt.tmmu.edu.cn/html/202407001.html |
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| author | SUN Changjin TONG Fei WU Yi |
| author_facet | SUN Changjin TONG Fei WU Yi |
| author_sort | SUN Changjin |
| collection | DOAJ |
| description | 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.
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| format | Article |
| id | doaj-art-cc4e91398a5f41d2b65edf7edfe3c63f |
| institution | Kabale University |
| issn | 2097-0927 |
| language | zho |
| publishDate | 2025-01-01 |
| publisher | Editorial Office of Journal of Army Medical University |
| record_format | Article |
| series | 陆军军医大学学报 |
| spelling | doaj-art-cc4e91398a5f41d2b65edf7edfe3c63f2025-01-13T09:09:25ZzhoEditorial Office of Journal of Army Medical University陆军军医大学学报2097-09272025-01-01471929910.16016/j.2097-0927.202407001Design of a mammography X-ray image classification assistant system adapted to Chinese populationSUN Changjin0TONG Fei1WU Yi2 Department of Medical Imaging, Army Medical Center of PLA/Daping Hospital of Third Military Medical University， Chongqing， ChinaDepartment of Digital Medicine， Faculty of Biomedical Engineering and Imaging Medicine， Army Medical University （Third Military Medical University）， Chongqing, Army Medical Center of PLA/Daping Hospital of Third Military Medical University， Chongqing， ChinaDepartment of Medical Engineering, Army Medical Center of PLA/Daping Hospital of Third Military Medical University， Chongqing， ChinaObjective 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. https://aammt.tmmu.edu.cn/html/202407001.htmlmammographydeep learningx-raycomputer assistance |
| spellingShingle | SUN Changjin TONG Fei WU Yi Design of a mammography X-ray image classification assistant system adapted to Chinese population 陆军军医大学学报 mammography deep learning x-ray computer assistance |
| title | Design of a mammography X-ray image classification assistant system adapted to Chinese population |
| title_full | Design of a mammography X-ray image classification assistant system adapted to Chinese population |
| title_fullStr | Design of a mammography X-ray image classification assistant system adapted to Chinese population |
| title_full_unstemmed | Design of a mammography X-ray image classification assistant system adapted to Chinese population |
| title_short | Design of a mammography X-ray image classification assistant system adapted to Chinese population |
| title_sort | design of a mammography x ray image classification assistant system adapted to chinese population |
| topic | mammography deep learning x-ray computer assistance |
| url | https://aammt.tmmu.edu.cn/html/202407001.html |
| work_keys_str_mv | AT sunchangjin designofamammographyxrayimageclassificationassistantsystemadaptedtochinesepopulation AT tongfei designofamammographyxrayimageclassificationassistantsystemadaptedtochinesepopulation AT wuyi designofamammographyxrayimageclassificationassistantsystemadaptedtochinesepopulation |