A fully integrated computer-aided diagnosis system for digital X-ray mammograms via deep learning detection, segmentation, and classification. (September 2018)

• Record Type: Journal Article
• Authors: Al-antari, Mugahed A.; Al-masni, Mohammed A.; Choi, Mun-Taek; Han, Seung-Moo; Kim, Tae-Seong
• Is Part Of: International journal of medical informatics. Volume 117(2018)
• Page Start: 44
• Page End: 54
• Publication Date: 2018-09
• Subjects: Computer-aided diagnosis (CAD) -- Mass detection -- You-only-look-once (YOLO) -- Mass segmentation -- Full resolution convolutional network (FrCN) -- Deep learning; Medical informatics -- Periodicals; Information science -- Periodicals; Computers -- Periodicals; Medical technology -- Periodicals; Medical Informatics -- Periodicals; Technology, Medical -- Periodicals; Computers; Information science; Medical informatics; Medical technology; Electronic journals; Periodicals; Electronic journals; 610.285
• DOI: 10.1016/j.ijmedinf.2018.06.003
• Languages: English
• ISSNs: 1386-5056

Abstract

Graphical abstract: Highlights: A Fully integrated Computer-Aided Diagnosis (CAD) system based on deep learning is presented. Deep model based YOLO is adopted to accurately detect the masses from the entire mammograms. A newly deep model based on FrCN is utilized to segment the mass lesions pixel-to-pixel. A deep CNN model is utilized to recognize the mass either as benign or malignant. Abstract: A computer-aided diagnosis (CAD) system requires detection, segmentation, and classification in one framework to assist radiologists efficiently in an accurate diagnosis. In this paper, a completely integrated CAD system is proposed to screen digital X-ray mammograms involving detection, segmentation, and classification of breast masses via deep learning methodologies. In this work, to detect breast mass from entire mammograms, You-Only-Look-Once (YOLO), a regional deep learning approach, is used. To segment the mass, full resolution convolutional network (FrCN), a new deep network model, is proposed and utilized. Finally, a deep convolutional neural network (CNN) is used to recognize the mass and classify it as either benign or malignant. To evaluate the proposed integrated CAD system in terms of the accuracies of detection, segmentation, and classification, the publicly available and annotated INbreast database was utilized. The evaluation results of the proposed CAD system via four-fold cross-validation tests show that a mass detection accuracy of 98.96%, Matthews correlation coefficient (MCC) of 97.62%, and F1-score of 99.24% are achieved with the INbreast dataset. Moreover, the mass segmentation results via FrCN produced an overall accuracy of 92.97%, MCC of 85.93%, and Dice (F1-score) of 92.69% and Jaccard similarity coefficient metrics of 86.37%, respectively. The detected and segmented masses were classified via CNN and achieved an overall accuracy of 95.64%, AUC of 94.78%, MCC of 89.91%, and F1-score of 96.84%, respectively. Our results demonstrate that the proposed CAD system, through all stages of detection, segmentation, and classification, outperforms the latest conventional deep learning methodologies. Our proposed CAD system could be used to assist radiologists in all stages of detection, segmentation, and classification of breast masses.