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DenseNet for Breast Tumor Classification in Mammographic Images

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Bioengineering and Biomedical Signal and Image Processing (BIOMESIP 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12940))

Abstract

Breast cancer screening is an efficient method to detect breast lesions early. The common screening techniques are tomosynthesis and mammography images. However, the traditional manual diagnosis requires an intense workload for pathologists, and hence is prone to diagnostic errors. Thus, the aim of this study was to build a deep convolutional neural network method for automatic detection, segmentation, and classification of breast lesions in mammography images. Based on deep learning the Mask-CNN (RoIAlign) method was developed to automate RoI segmentation. Then feature extraction, selection and classification were carried out by the DenseNet architecture. Finally, the precision and accuracy of the model was evaluated by the AUC, accuracy and precision metrics. To summarize, the findings of this study show that the methodology may improve the diagnosis and efficiency in automatic tumor localization through medical image classification.

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Acknowledgement

VL would like to thank the natural sciences and engineering research council of Canada (NSERC) for a discovery grant. Y.J.G. and D.C.M. acknowledge the research support of Universidad Técnica Particular de Loja through the project PROY_INV_QUI_2020_2784 and the CSIC grant PTA2019–017113-1/AEI/https://doi.org/10.13039/501100011033.

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Authors and Affiliations

  1. Departamento de Química y Ciencias Exactas, Universidad Técnica Particular de Loja, San Cayetano Alto s/n CP1101608, Loja, Ecuador

    Yuliana Jiménez Gaona & Darwin Castillo Malla

  2. Imagen Molecular i3M, Universitat Politècnica de València (UPV), 46022, Valencia, Spain

    Yuliana Jiménez Gaona, María José Rodriguez-Alvarez, Hector Espino-Morato & Darwin Castillo Malla

  3. Theoretical and Experimental Epistemology Lab, School of Optometry and Vision Science, Waterloo, Canada

    Yuliana Jiménez Gaona, Darwin Castillo Malla & Vasudevan Lakshminarayanan

  4. Department of Systems Design Engineering, Physics, and Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, N2L3G1, Canada

    Vasudevan Lakshminarayanan

  5. Consejo Superior de Investigaciones Científicas (CSIC), 46022, Valencia, Spain

    Hector Espino-Morato

Authors
  1. Yuliana Jiménez Gaona
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  2. María José Rodriguez-Alvarez
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  3. Hector Espino-Morato
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  4. Darwin Castillo Malla
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  5. Vasudevan Lakshminarayanan
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Corresponding authors

Correspondence to Yuliana Jiménez Gaona or Vasudevan Lakshminarayanan .

Editor information

Editors and Affiliations

  1. University of Granada, Granada, Spain

    Ignacio Rojas

  2. University of Granada, Granada, Spain

    Daniel Castillo-Secilla

  3. University of Granada, Granada, Spain

    Luis Javier Herrera

  4. Universidad de Granada, Granada, Granada, Spain

    Héctor Pomares

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Jiménez Gaona, Y., Rodriguez-Alvarez, M.J., Espino-Morato, H., Castillo Malla, D., Lakshminarayanan, V. (2021). DenseNet for Breast Tumor Classification in Mammographic Images. In: Rojas, I., Castillo-Secilla, D., Herrera, L.J., Pomares, H. (eds) Bioengineering and Biomedical Signal and Image Processing. BIOMESIP 2021. Lecture Notes in Computer Science(), vol 12940. Springer, Cham. https://doi.org/10.1007/978-3-030-88163-4_16

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  • DOI: https://doi.org/10.1007/978-3-030-88163-4_16

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