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

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 ea...

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Bibliographic Details
Published inJournal of imaging Vol. 5; no. 3; p. 37
Main Authors Tsochatzidis, Lazaros, Costaridou, Lena, Pratikakis, Ioannis
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 13.03.2019
MDPI
Subjects
Artificial neural networks
Breast cancer
CAD
Classification
Comparative studies
convolutional neural networks
Datasets
Deep learning
Diagnosis
Machine learning
Mammography
Mortality
Neural networks
Neurons
Performance evaluation
Training
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ISSN2313-433X
2313-433X
DOI10.3390/jimaging5030037

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Summary: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.
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ISSN:2313-433X
2313-433X
DOI:10.3390/jimaging5030037