Abstract
For computer-aided diagnosis (CAD), detection, segmentation, and classification from medical imagery are three key components to efficiently assist physicians for accurate diagnosis. In this chapter, a completely integrated CAD system based on deep learning is presented to diagnose breast lesions from digital X-ray mammograms involving detection, segmentation, and classification. To automatically detect breast lesions from mammograms, a regional deep learning approach called You-Only-Look-Once (YOLO) is used. To segment breast lesions, full resolution convolutional network (FrCN), a novel segmentation model of deep network, is implemented and used. Finally, three conventional deep learning models including regular feedforward CNN, ResNet-50, and InceptionResNet-V2 are separately adopted and used to classify or recognize the detected and segmented breast lesion as either benign or malignant. To evaluate the integrated CAD system for detection, segmentation, and classification, the publicly available and annotated INbreast database is used over fivefold cross-validation tests. The evaluation results of the YOLO-based detection achieved detection accuracy of 97.27%, Matthews’s correlation coefficient (MCC) of 93.93%, and F1-score of 98.02%. Moreover, the results of the breast lesion segmentation via FrCN achieved an overall accuracy of 92.97%, MCC of 85.93%, Dice (F1-score) of 92.69%, and Jaccard similarity coefficient of 86.37%. The detected and segmented breast lesions are classified via CNN, ResNet-50, and InceptionResNet-V2 achieving an average overall accuracies of 88.74%, 92.56%, and 95.32%, respectively. The performance evaluation results through all stages of detection, segmentation, and classification show that the integrated CAD system outperforms the latest conventional deep learning methodologies. We conclude that our CAD system could be used to assist radiologists over all stages of detection, segmentation, and classification for diagnosis of breast lesions.
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References
Siegel RL, Miller KD, Jemal A (2017) Cancer statistics, 2017. CA Cancer J Clin 67(1):7–30
Al-antari MA, Al-masni MA, Park SU, Park JH, Kadah YM, Han SM, Kim T-S (2016) Automatic computer-aided diagnosis of breast cancer in digital mammograms via deep belief network. In: Global conference on engineering and applied science (GCEAS), Japan, pp 1306–1314
Jill J (2015) Breast cancer screening guidelines in the United States. JAMA 314:1658–1658
Al-antari MA, Al-masni MA, Park SU, Park JH, Metwally MK, Kadah YM, Han SM, Kim T-S (2017) An automatic computer-aided diagnosis system for breast cancer in digital mammograms via deep belief network. J Med Biol Eng. https://doi.org/10.1007/s40846-017-0321-6
Al-antaria MA, Al-masni MA, Choi M-T, Han S-M (2018) A fully integrated computer-aided diagnosis system for digital X-ray mammograms via deep learning detection, segmentation, and classification. Int J Med Inform 117:44–54
Casellas-Grau A, Vives J, Font A, Ochoa C (2016) Positive psychological functioning in breast cancer: an integrative review. Breast 27:136–168
Al-Masni MA, Al-Antari MA, Park JM, Gi G, Kim TY, Rivera P, Valarezo E, Choi MT, Han SM, Kim TS (2018) Simultaneous detection and classification of breast masses in digital mammograms via a deep learning YOLO-based CAD system. Comput Methods Programs Biomed 157:85–94
Dhungel N, Carneiro G, Bradley AP (2017) A deep learning approach for the analysis of masses in mammograms with minimal user intervention. Med Image Anal 37(1):114–128
Yassin NI, Omran S, Houby EM, Allam H (2018) Machine learning techniques for breast cancer computer aided diagnosis using different image modalities: a systematic review. Comput Methods Programs Biomed 156:25–45
Chakraborty J, Midya A, Rabidas R (2018) Computer-aided detection and diagnosis of mammographic masses using multi-resolution analysis of oriented tissue patterns. Exp Syst Appl 99(1):168–179
Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: unified, real-time object detection. In: 2016 IEEE conference on computer vision and pattern recognition, pp 779–788
Moreira I, Amaral I, Domingues I, Cardoso A, Cardoso M, Cardoso J (2012) INbreast: toward a full-field digital mammographic database. Acad Radiol 19(2):236–248
Jiao Z, Gao X, Wang Y, Li J (2016) A deep feature based framework for breast masses classification. Neurocomputing 197(C):221–231
Carneiro G, Nascimento J, Bradley AP (2017) Automated analysis of unregistered multi-view mammograms with deep learning. IEEE Trans Med Imaging 36(11):2355–2365
Al-masni MA, Al-antari MA, Park JM, Gi G, Kim TY, Rivera P, Valarezo E, Han S-M, Kim T-S (2017) Detection and classification of the breast abnormalities in digital mammograms via regional Convolutional Neural Network. In: 39th annual international conference of the IEEE engineering in medicine and biology society (EMBC’17), Jeju Island, South Korea, 2017, pp 1230–1236
Ayelet A-B, Karlinsky L, Alpert S, Hasoul S, Ben-Ari R, Barkan E (2016) A region based convolutional network for tumor detection and classification in breast mammography. In: International workshop on large-scale annotation of biomedical data and expert label synthesis. Springer, Athens, pp 197–205
Cardoso JS, Domingues I, Oliveira HP (2015) Closed shortest path in the original coordinates with an application to breast cancer. Int J Pattern Recognit Artif Intell 29(1):2
Yuan Y, Chao M, Lo Y-C (2017) Automatic skin lesion segmentation using deep fully convolutional networks with Jaccard distance. IEEE Trans Med Imaging 36(9):1876–1886
Ronneberger O, Fischer P, Brox T (2015) U-Net: convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention
Badrinarayanan V, Kendall A, Cipoll R (2016) SegNet: a deep convolutional encoder-decoder architecture for image segmentation. arXiv preprint arXiv:1511.00561
Kooi T, Litjens G, van Ginneken B, Gubern-Mérida A, Sánchez CI, Mann R, den Heeten A, Karssemeijer N (2017) Large scale deep learning for computer aided detection of mammographic lesions. Med Image Anal 35:303–312
Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. In: 25th international conference on neural information processing systems, USA, 2012, pp 1097–1105
Shelhamer E, Long J, Darrell T (2017) Fully Convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell 39(4):640–651
He K, Zhang X, Ren S, Sun J (2015) Deep residual learning for image recognition. arXiv:1602.07261v2, pp 770–787
Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprintarXiv:1409.1556
Szegedy V, Ioffe S, Vanhoucke V (2016) Inception-v4, Inception-ResNet and the impact of residual connections on learning. arXiv:1602.07261v2 [cs.CV]
Lab L (2017) Theano. University of Montreal [Online]. Available: http://deeplearning.net/software/theano/tutorial/. Accessed 10 2017
Chollet F (2017) Keras: the Python deep learning library. MIT, [Online]. Available: https://keras.io/. Accessed 10 2017
Google Brain Team, TensorFlow, 9 11 2017. [Online]. Available: www.tensorflow.org. Accessed 10 2017
Kozegar E, Soryani M, Minaei B, Inês D (2013) Assessment of a novel mass detection algorithm in mammograms. J Cancer Res Ther:592–600
Acknowledgements
This work was supported by International Collaborative Research and Development Programme funded by the Ministry of Trade, Industry and Energy (MOTIE, Korea) (N0002252). This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (2019R1A2C1003713).
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Al-antari, M.A., Al-masni, M.A., Kim, TS. (2020). Deep Learning Computer-Aided Diagnosis for Breast Lesion in Digital Mammogram. In: Lee, G., Fujita, H. (eds) Deep Learning in Medical Image Analysis . Advances in Experimental Medicine and Biology, vol 1213. Springer, Cham. https://doi.org/10.1007/978-3-030-33128-3_4
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