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J. Imaging 2019, 5(3), 37; https://doi.org/10.3390/jimaging5030037

Deep Learning for Breast Cancer Diagnosis from Mammograms—A Comparative Study

1
Visual Computing Group, Department of Electrical and Computer Engineering, Democritus University of Thrace, 67100 Xanthi, Greece
2
Department of Medical Physics, School of Medicine, University of Patras, 26504 Patras, Greece
*
Author to whom correspondence should be addressed.
Received: 27 January 2019 / Revised: 5 March 2019 / Accepted: 7 March 2019 / Published: 13 March 2019
(This article belongs to the Special Issue Modern Advances in Image Fusion)
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Abstract

Deep convolutional neural networks (CNNs) are investigated in the context of computer-aided diagnosis (CADx) of breast cancer. State-of-the-art CNNs are trained and evaluated on two mammographic datasets, consisting of ROIs depicting benign or malignant mass lesions. The performance evaluation of each examined network is addressed in two training scenarios: the first involves initializing the network with pre-trained weights, while for the second the networks are initialized in a random fashion. Extensive experimental results show the superior performance achieved in the case of fine-tuning a pretrained network compared to training from scratch. View Full-Text
Keywords: mammography; breast cancer; deep learning; convolutional neural networks; CAD mammography; breast cancer; deep learning; convolutional neural networks; CAD
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Tsochatzidis, L.; Costaridou, L.; Pratikakis, I. Deep Learning for Breast Cancer Diagnosis from Mammograms—A Comparative Study. J. Imaging 2019, 5, 37.

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