Abstract
This paper describes the method of building a X-ray database for Mammography on Vietnamese patients that we collected at Hanoi Medical University Hospital. This dataset has 4664 images (Dicom) corresponding to 1161 standard patients with uniform distribution according to BIRAD from 0 to 5. This paper also presents the method of detecting Region of Interest (ROI) in mammogram based on Mask R-CNN architecture. The method of determining the ROI for accuracy mAP@0.5 = 0.8109 and the accuracy of classification BIRAD levels is 58.44%.
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Acknowledgements
This work is supported by foundation of the research and development contract between Thang Long University and Hanoi Medical University Hospital, Vietnam dated on 27 November, 2018 on “Developing a support system for diagnosis of breast cancer based on X-Ray using Artificial Intelligence”.
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Thang, N.D. et al. (2021). Building a X-ray Database for Mammography on Vietnamese Patients and automatic Detecting ROI Using Mask-RCNN. In: Kreinovich, V., Hoang Phuong, N. (eds) Soft Computing for Biomedical Applications and Related Topics. Studies in Computational Intelligence, vol 899. Springer, Cham. https://doi.org/10.1007/978-3-030-49536-7_27
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DOI: https://doi.org/10.1007/978-3-030-49536-7_27
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