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

Al-antari, Mugahed A., Al-masni, Mohammed A., Choi, Mun-Taek, Han, Seung-Moo, Kim, Tae-Seong·2018·International Journal of Medical Informatics
DOI 10.1016/j.ijmedinf.2018.06.003
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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.

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Metadata

Authors
Al-antari, M. A., Al-masni, M. A., Choi, M.-T., Han, S.-M., & Kim, T.-S.
Venue
International Journal of Medical Informatics
Publisher
Elsevier BV
Published
2018-09
Volume
117
Pages
44-54
ISSN
1386-5056 (ISSN-L)
DOI
10.1016/j.ijmedinf.2018.06.003
OpenAlex
W2809504579
Cited by
408
References
11
FWCI
22.07
Type
journal article
Language
en
Open access
closed
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)

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CoChat. (2026). Citation check: A fully integrated computer-aided diagnosis system for digital X-ray mammograms via deep learning detection, segmentation, and classification. https://app.cochat.ai/papers/10.1016/j.ijmedinf.2018.06.003
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Al-antari, M. A., Al-masni, M. A., Choi, M.-T., Han, S.-M., & Kim, T.-S. (2018). A fully integrated computer-aided diagnosis system for digital X-ray mammograms via deep learning detection, segmentation, and classification. International Journal of Medical Informatics. https://doi.org/10.1016/j.ijmedinf.2018.06.003

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