Automated mass detection in mammograms using cascaded deep learning and random forests

Dhungel, Neeraj, Carneiro, Gustavo and Bradley, Andrew P. (2016). Automated mass detection in mammograms using cascaded deep learning and random forests. In: 2015 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2015. International Conference on Digital Image Computing: Techniques and Applications, DICTA 2015, Adelaide, Australia, (). 23-25 November 2015. doi:10.1109/DICTA.2015.7371234


Author Dhungel, Neeraj
Carneiro, Gustavo
Bradley, Andrew P.
Title of paper Automated mass detection in mammograms using cascaded deep learning and random forests
Conference name International Conference on Digital Image Computing: Techniques and Applications, DICTA 2015
Conference location Adelaide, Australia
Conference dates 23-25 November 2015
Convener IEEE
Proceedings title 2015 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2015
Place of Publication Piscataway, NJ, United States
Publisher Institute of Electrical and Electronics Engineers Inc.
Publication Year 2016
Year available 2016
Sub-type Fully published paper
DOI 10.1109/DICTA.2015.7371234
Open Access Status Not Open Access
ISBN 9781467367950
Total pages 8
Collection year 2017
Language eng
Abstract/Summary Mass detection from mammograms plays a crucial role as a pre-processing stage for mass segmentation and classification. The detection of masses from mammograms is considered to be a challenging problem due to their large variation in shape, size, boundary and texture and also because of their low signal to noise ratio compared to the surrounding breast tissue. In this paper, we present a novel approach for detecting masses in mammograms using a cascade of deep learning and random forest classifiers. The first stage classifier consists of a multi-scale deep belief network that selects suspicious regions to be further processed by a two-level cascade of deep convolutional neural networks. The regions that survive this deep learning analysis are then processed by a two-level cascade of random forest classifiers that use morphological and texture features extracted from regions selected along the cascade. Finally, regions that survive the cascade of random forest classifiers are combined using connected component analysis to produce state-of-The-Art results. We also show that the proposed cascade of deep learning and random forest classifiers are effective in the reduction of false positive regions, while maintaining a high true positive detection rate. We tested our mass detection system on two publicly available datasets: DDSM-BCRP and INbreast. The final mass detection produced by our approach achieves the best results on these publicly available datasets with a true positive rate of 0.96 ± 0.03 at 1.2 false positive per image on INbreast and true positive rate of 0.75 at 4.8 false positive per image on DDSM-BCRP.
Keyword Mass detection
Mammogram
Mass segmentation and classification
Deep learning
Random forest classifiers
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status UQ

 
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