Simultaneous detection and classification of breast masses in digital mammograms via a deep learning YOLO-based CAD system.

Journal: Computer methods and programs in biomedicine
Published Date: Jan 31, 2018

Abstract

BACKGROUND AND OBJECTIVE: Automatic detection and classification of the masses in mammograms are still a big challenge and play a crucial role to assist radiologists for accurate diagnosis. In this paper, we propose a novel Computer-Aided Diagnosis (CAD) system based on one of the regional deep learning techniques, a ROI-based Convolutional Neural Network (CNN) which is called You Only Look Once (YOLO). Although most previous studies only deal with classification of masses, our proposed YOLO-based CAD system can handle detection and classification simultaneously in one framework.

Authors

  • Mohammed A Al-Masni
    Department of Biomedical Engineering, College of Electronics and Information, Kyung Hee University, Yongin, Republic of Korea.
  • Mugahed A Al-Antari
    Department of Computer Science and Engineering, College of Software, Kyung Hee University, 1732, Deogyeong-daero, Giheung-gu, Yongin-si, Gyeonggi-do 17104 Republic of Korea.
  • Jeong-Min Park
    Department of Biomedical Engineering, College of Electronics and Information, Kyung Hee University, Yongin, Republic of Korea. Electronic address: jmpark@khu.ac.kr.
  • Geon Gi
    Department of Biomedical Engineering, College of Electronics and Information, Kyung Hee University, Yongin, Republic of Korea. Electronic address: geon@khu.ac.kr.
  • Tae-Yeon Kim
    Department of Biomedical Engineering, College of Electronics and Information, Kyung Hee University, Yongin, Republic of Korea. Electronic address: kty@khu.ac.kr.
  • Patricio Rivera
    Department of Biomedical Engineering, College of Electronics and Information, Kyung Hee University, Yongin, Republic of Korea. Electronic address: patoalejor@khu.ac.kr.
  • Edwin Valarezo
    Department of Biomedical Engineering, College of Electronics and Information, Kyung Hee University, Yongin, Republic of Korea. Electronic address: edgivala@khu.ac.kr.
  • Mun-Taek Choi
    School of Mechanical Engineering, Sungkyunkwan University, Republic of Korea. Electronic address: mtchoi@skku.edu.
  • Seung-Moo Han
    Department of Biomedical Engineering, College of Electronics and Information, Kyung Hee University, Yongin, Republic of Korea.
  • Tae-Seong Kim
    Department of Biomedical Engineering, College of Electronics and Information, Kyung Hee University, Yongin, Republic of Korea.

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