A Robust Approach for Breast Cancer Classification from DICOM Images
Thanh-Nghia Nguyen, Thanh-Tam Nguyen, Nguyễn Thanh Hải, Thanh-Tam Nguyen, Nguyễn Thanh Hải, Nguyễn Thanh Hải, Ba-Viet Ngo · Engineering Technology & Applied Science Research · 2025
The number of breast cancer patients is rapidly increasing worldwide, with Asia currently accounting for 45% of global breast cancer cases. In addition, the number of breast cancer cases is expected to increase by 21.0%, and the mortality rate is projected to increase by 27.8% during the 2020-2030 period. This paper proposes a method for classifying breast cancer from Digital Imaging and Communications in Medicine (DICOM) images. In particular, an image segmentation technique was developed for extracting the breast region from DICOM images of varying sizes. The extracted images were then enhanced using multiple augmentation techniques to improve classification performance. Finally, a deep learning network was applied to classify breast cancer from the processed DICOM images. The VinDr-Mammo dataset was used to evaluate the effectiveness of the proposed method, and the experimental results showed an accuracy of 81.45%, demonstrating that the proposed approach is highly suitable for breast cancer detection and classification.