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Classification of Mammograms Using Convolutional Neural Network Based Feature Extraction

  • Taye Girma Debelee
  • Mohammadreza Amirian
  • Achim Ibenthal
  • Günther Palm
  • Friedhelm Schwenker
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 244)

Abstract

Breast cancer is the most common cause of death among women in the entire world and the second cause of death after lung cancer. The use of automatic breast cancer detection and classification might possibly enhance the survival rate of the patients through starting early treatment. In this paper, the convolutional Neural Networks (CNN) based feature extraction method is proposed. The features dimensionality was reduced using Principal Component Analysis (PCA). The reduced features are given to the K-Nearest Neighbors (KNN) to classify mammograms as normal or abnormal using 10-fold cross-validation. The experimental result of the proposed approach performed on Mammography Image Analysis Society (MIAS) and Digital Database for Screening Mammography (DDSM) datasets were found to be promising compared to previous studies in the area of image processing, artificial intelligence and CNN with an accuracy of 98.75\(\%\) and 98.90\(\%\) on MIAS and DDSM dataset respectively.

Keywords

Breast cancer Mammogram CNN K-nearest neighbour Feature extraction 

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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018

Authors and Affiliations

  • Taye Girma Debelee
    • 1
    • 3
  • Mohammadreza Amirian
    • 1
  • Achim Ibenthal
    • 2
  • Günther Palm
    • 1
  • Friedhelm Schwenker
    • 1
  1. 1.Institute of Neural Information ProcessingUlm UniversityUlmGermany
  2. 2.Adama Science and Technology UniversityAdamaEthiopia
  3. 3.Addis Abeba Science and Technology UniversityAddis AbebaEthiopia

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