Convolutional neural networks for mammography mass lesion classification

Feature extraction is a fundamental step when mammography image analysis is addressed using learning based approaches. Traditionally, problem dependent handcrafted features are used to represent the content of images. An alternative approach successfully applied in other domains is the use of neural...

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Bibliographic Details
Main Authors: Arevalo, John, Gonzalez, Fabio A., Ramos-Pollan, Raul, Oliveira, Jose L., Guevara Lopez, Miguel Angel
Format: Conference Proceeding
Language:English
Subjects:
Breast cancer
Feature extraction
Lesions
Machine learning
Shape
Training
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Summary:Feature extraction is a fundamental step when mammography image analysis is addressed using learning based approaches. Traditionally, problem dependent handcrafted features are used to represent the content of images. An alternative approach successfully applied in other domains is the use of neural networks to automatically discover good features. This work presents an evaluation of convolutional neural networks to learn features for mammography mass lesions before feeding them to a classification stage. Experimental results showed that this approach is a suitable strategy outperforming the state-of-the-art representation from 79.9% to 86% in terms of area under the ROC curve.
ISSN:1094-687X
2694-0604
DOI:10.1109/EMBC.2015.7318482