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Mammogram Classification Schemes by Using Convolutional Neural Networks

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 798))

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Abstract

This work presents the comparison of two schemes of mammogram classification based on convolutional neural networks (CNN). The main difference between these two classification schemes relies on the number of bits per image pixel, the feature extraction techniques and the number of neurons in the fully connected layer. We use 1070 mammograms from the Digital Database for Screening Mammography (DDSM), which are divided into two categories: benign and malignant mammograms. We use CNN for classification by applying the open source library TensorFlow which is configured on the high level library Keras. In order to tune our classification model parameters, we apply random and grid search algorithms, by combining the batch size, the number of layers, the learning rate and three optimizers: Adadelta, RMSProp and SGD. We evaluate the classification algorithm performances through the accuracy and two loss functions: Categorical Cross-Entropy and Mean Squared Error. The model with the best accuracy has 85.00%, and a mean squared error of 15.00%.

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Author information

Authors and Affiliations

  1. Unidad Académica de Ingeniería Civil, Universidad Técnica de Machala, Machala, El Oro, 070210, Ecuador

    Danny Soriano, Carlos Aguilar, Eduardo Tusa, Wilmer Rivas & Maritza Pinta

  2. Unidad Académica de Ciencias Agropecuarias, Universidad Técnica de Machala, Machala, El Oro, 070210, Ecuador

    Ivan Ramirez-Morales

Authors
  1. Danny Soriano
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  2. Carlos Aguilar
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  3. Ivan Ramirez-Morales
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  4. Eduardo Tusa
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  5. Wilmer Rivas
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  6. Maritza Pinta
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Corresponding author

Correspondence to Eduardo Tusa .

Editor information

Editors and Affiliations

  1. Eindhoven University of Technology, Eindhoven, The Netherlands

    Miguel Botto-Tobar

  2. Universidad Técnica de Babahoyo, Babahoyo, Ecuador

    Nelly Esparza-Cruz

  3. Universidad Técnica de Babahoyo, Babahoyo, Ecuador

    Joffre León-Acurio

  4. Universidad Técnica de Babahoyo, Babahoyo, Ecuador

    Narcisa Crespo-Torres

  5. Universidad Técnica de Babahoyo, Babahoyo, Ecuador

    Marola Beltrán-Mora

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Soriano, D., Aguilar, C., Ramirez-Morales, I., Tusa, E., Rivas, W., Pinta, M. (2018). Mammogram Classification Schemes by Using Convolutional Neural Networks. In: Botto-Tobar, M., Esparza-Cruz, N., León-Acurio, J., Crespo-Torres, N., Beltrán-Mora, M. (eds) Technology Trends. CITT 2017. Communications in Computer and Information Science, vol 798. Springer, Cham. https://doi.org/10.1007/978-3-319-72727-1_6

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  • DOI: https://doi.org/10.1007/978-3-319-72727-1_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-72726-4

  • Online ISBN: 978-3-319-72727-1

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