Journal
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
Volume 157, Issue -, Pages 85-94Publisher
ELSEVIER IRELAND LTD
DOI: 10.1016/j.cmpb.2018.01.017
Keywords
Breast cancer; Mass detection and classification; Computer Aided Diagnosis; Deep learning; You Only Look Once (YOLO)
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Funding
- Center for Integrated Smart Sensors - Ministry of Science, ICT & Future Planning as a Global Frontier Project [CISS- 2011-0031863]
- International Collaborative Research and Development Programme (Ministry of Trade, Industry and Energy (MOTIE, Korea)) [N0002252]
- Korea Evaluation Institute of Industrial Technology (KEIT) [N0002252] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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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.
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