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

被引:285
作者
Al-masni, Mohammed A. [1 ]
Al-antari, Mugahed A. [1 ]
Park, Jeong-Min [1 ]
Gi, Geon [1 ]
Kim, Tae-Yeon [1 ]
Rivera, Patricio [1 ]
Valarezo, Edwin [1 ]
Choi, Mun-Taek [2 ]
Han, Seung-Moo [1 ]
Kim, Tae-Seong [1 ]
机构
[1] Kyung Hee Univ, Coll Elect & Informat, Dept Biomed Engn, Yongin, South Korea
[2] Sungkyunkwan Univ, Sch Mech Engn, Seoul, South Korea
关键词
Breast cancer; Mass detection and classification; Computer Aided Diagnosis; Deep learning; You Only Look Once (YOLO); SEGMENTATION;
D O I
10.1016/j.cmpb.2018.01.017
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
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. Methods: The proposed CAD system contains four main stages: preprocessing of mammograms, feature extraction utilizing deep convolutional networks, mass detection with confidence, and finally mass classification using Fully Connected Neural Networks (FC-NNs). In this study, we utilized original 600 mammograms from Digital Database for Screening Mammography (DDSM) and their augmented mammograms of 2,400 with the information of the masses and their types in training and testing our CAD. The trained YOLO-based CAD system detects the masses and then classifies their types into benign or malignant. Results: Our results with five-fold cross validation tests show that the proposed CAD system detects the mass location with an overall accuracy of 99.7%. The system also distinguishes between benign and malignant lesions with an overall accuracy of 97%. Conclusions: Our proposed system even works on some challenging breast cancer cases where the masses exist over the pectoral muscles or dense regions. (C) 2018 Elsevier B.V. All rights reserved.
引用
收藏
页码:85 / 94
页数:10
相关论文
共 38 条
  • [1] A Region Based Convolutional Network for Tumor Detection and Classification in Breast Mammography
    Akselrod-Ballin, Ayelet
    Karlinsky, Leonid
    Alpert, Sharon
    Hasoul, Sharbell
    Ben-Ari, Rami
    Barkan, Ella
    [J]. DEEP LEARNING AND DATA LABELING FOR MEDICAL APPLICATIONS, 2016, 10008 : 197 - 205
  • [2] Al-antari M. A., 2017, J MED BIOL ENG
  • [3] Al-antari M. A., 2016, GLOB C ENG APPL SCI
  • [4] Al-antari MA., 2017, J SCI ENG, V04, P114
  • [5] Al-Masni M. A, 2017, ENG MED BIOL SOC EMB
  • [6] Representation learning for mammography mass lesion classification with convolutional neural networks
    Arevalo, John
    Gonzalez, Fabio A.
    Ramos-Pollan, Raul
    Oliveira, Jose L.
    Guevara Lopez, Miguel Angel
    [J]. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, 2016, 127 : 248 - 257
  • [7] Chest pathology identification using deep feature selection with non-medical training
    Bar, Yaniv
    Diamant, Idit
    Wolf, Lior
    Lieberman, Sivan
    Konen, Eli
    Greenspan, Hayit
    [J]. COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING-IMAGING AND VISUALIZATION, 2018, 6 (03) : 259 - 263
  • [8] Automated Analysis of Unregistered Multi-View Mammograms With Deep Learning
    Carneiro, Gustavo
    Nascimento, Jacinto
    Bradley, Andrew P.
    [J]. IEEE TRANSACTIONS ON MEDICAL IMAGING, 2017, 36 (11) : 2355 - 2365
  • [9] Segmentation of Bone Structure in X-ray Images using Convolutional Neural Network
    Cernazanu-Glavan, Cosmin
    Holban, Stefan
    [J]. ADVANCES IN ELECTRICAL AND COMPUTER ENGINEERING, 2013, 13 (01) : 87 - 94
  • [10] Observer studies involving detection and localization: Modeling, analysis, and validation
    Chakraborty, DP
    Berbaum, KS
    [J]. MEDICAL PHYSICS, 2004, 31 (08) : 2313 - 2330