Simultaneous detection and classification of breast masses in digital mammograms via a deep learning YOLO-based CAD system. (April 2018)

• Record Type: Journal Article
• Authors: Al-masni, Mohammed A.; Al-antari, Mugahed A.; Park, Jeong-Min; Gi, Geon; Kim, Tae-Yeon; Rivera, Patricio; Valarezo, Edwin; Choi, Mun-Taek; Han, Seung-Moo; Kim, Tae-Seong
• Is Part Of: Computer methods and programs in biomedicine. Volume 157(2018)
• Page Start: 85
• Page End: 94
• Publication Date: 2018-04
• Subjects: Breast cancer -- Mass detection and classification -- Computer Aided Diagnosis -- Deep learning -- You Only Look Once (YOLO); Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2018.01.017
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: A novel computer-aided diagnosis system based on deep learning techniques is proposed. The proposed YOLO-based CAD system simultaneously handles both detection and classification of breast cancer masses. YOLO-based CAD has a capability to handle most challenging cases of breast abnormalities. 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. 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.