Detecting Masses in Mammograms using Convolutional Neural Networks and Transfer Learning

Mor Yemini, Yaniv Zigel, Dror Lederman

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Scopus citations

Abstract

This paper addresses the problem of mass detection in mammograms. It has long ago been shown that computer-Aided diagnosis (CAD) schemes have the potential of improving breast cancer diagnosis performance. We propose a CAD scheme based on convolutional neural networks, using transfer representation learning and the Google Inception-V3 architecture. Artificially generated mammograms and data augmentation techniques are used to expand and balance the available database at train time. The performance of the proposed scheme is evaluated based on the receiver operating characteristics (ROC) curve. Areas under the ROC curve of 0.78 and 0.86 were obtained using artificially-generated mammograms and augmentation, respectively.

Original languageEnglish
Title of host publication2018 IEEE International Conference on the Science of Electrical Engineering in Israel, ICSEE 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538663783
DOIs
StatePublished - 20 Feb 2019
Event2018 IEEE International Conference on the Science of Electrical Engineering in Israel, ICSEE 2018 - Eilat, Israel
Duration: 12 Dec 201814 Dec 2018

Publication series

Name2018 IEEE International Conference on the Science of Electrical Engineering in Israel, ICSEE 2018

Conference

Conference2018 IEEE International Conference on the Science of Electrical Engineering in Israel, ICSEE 2018
Country/TerritoryIsrael
CityEilat
Period12/12/1814/12/18

Keywords

  • Breast cancer
  • convolutional neural networks
  • deep learning
  • mammogram
  • transfer learning

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