Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/107548
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Type: | Conference paper |
Title: | Automated Mass Detection in Mammograms Using Cascaded Deep Learning and Random Forests |
Author: | Dhungel, N. Carneiro, G. Bradley, A. |
Citation: | Proceedings of the 2015 International Conference on Digital Image Computing: Techniques and Applications, 2015, pp.160-167 |
Publisher: | IEEE |
Issue Date: | 2015 |
ISBN: | 9781467367950 |
Conference Name: | 2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA 2015) (23 Nov 2015 - 25 Nov 2015 : Adelaide, Australia) |
Statement of Responsibility: | Neeraj Dhungel, Gustavo Carneiro, Andrew P. Bradley |
Abstract: | 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. |
Keywords: | Feature extraction, mammography, machine learning, breast cancer, support vector machines, image resolution |
Rights: | © 2015 Crown |
DOI: | 10.1109/DICTA.2015.7371234 |
Grant ID: | http://purl.org/au-research/grants/arc/DP140102794 http://purl.org/au-research/grants/arc/FT110100623 |
Published version: | http://dx.doi.org/10.1109/dicta.2015.7371234 |
Appears in Collections: | Aurora harvest 3 Computer Science publications |
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