Earticle

초록

영어

Detecting mass lesions not only helps reduce the cost of treating breast cancer but also enhances the lifespan of patients. Various computer-aided detection (CAD) systems have been developed to assist physicians in detecting mass in mammograms for early cancer screening. In this paper, a method for suspicious massive lesion segmentation in patches is proposed, which modified UNet with EfficientNet as the encoder. The proposed architectures are evaluated on publicly available dataset, namely the Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM). The quantitative results show that the proposed architecture can achieve mass segmentation with segmentation ac- curacy, Dice and IoU scores of 95.23%, 92.56% and 88.81% respectively in patches extracted from CBIS-DDSM.

목차

Abstract
I. INTRODUCTION (HEADING 1)
II. PROPOSED METHOD
A. Unet
B. EfficientNet
C. Proposed EfficientNet-B0-Unet
III. EXPERIMENTS
A. Dataset
B. Preprocessing
C. Experimental details
D. Perfomance metrics
IV. RESULTS AND DISCUSSIONS
V. CONCLUSION
REFERENCES

키워드

저자

• Viet Dung Nguyen [ Biomedical Engineering Group, Department of Electronics, School of Electrical and Electronic Engineering Hanoi University of Science and Technology Hanoi, Vietnam ] Corresponding Author
• Thi Mai Nguyen [ Biomedical Engineering Group, Department of Electronics, School of Electrical and Electronic Engineering Hanoi University of Science and Technology Hanoi, Vietnam ]
• Sang Woong Lee [ Division of Software, School of Computing Gachon University Gyeonggido, Korea ]
• Ngoc Dung Bui [ Faculty of Information Technology University of Transport and Communications Hanoi, Vietnam ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004