Mass Detection in Mammograms Using a Robust Deep Learning Model

Vivek Kumar Singh*, Mohamed Abdel-Nasser, Hatem A. Rashwan, Farhan Akram, Rami Haffar, Nidhi Pandey, Md Mostafa Kamal Sarker, Sebastian Kohan, Josep Guma, Santiago Romani, Domenec Puig

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Computer-aided detection (CADe) methods can help radiologists detect breast cancer in its early stage. However, several breast mass detection methods have been proposed, they still produce a noticeable number of false positives (i.e., normal breast tissue is wrongly detected as a mass) due to the variability in breast density and mass size. In this paper, an automated detection method is proposed to detect breast masses from mammographic images using a modified Faster R-CNN detector based on Inception-ResNet-v2 feature extractor with a squeeze and excitation block. The squeeze and excitation mechanism provides channel inter-dependencies inside the Inception-ResNet-v2 feature extractor, which helps to extract low contrast texture features in mammogram images. Both qualitative and quantitative comparisons using INbreast dataset show that the proposed method outperforms the state-of-the-art methods. The proposed method yields the highest true positive rate (98.5%) and the lowest detection time (4 seconds). A visualization of the mass detection results can be found at https://youtu.be/PP2OldECuPY.

Original languageEnglish
Title of host publicationArtificial Intelligence Research and Development: Proceedings of the 22nd International Conference of the Catalan Association for Artificial Intelligence, CCIA 2019
EditorsJordi Sabater-Mir, Vicenc Torra, Isabel Aguilo, Manuel Gonzalez-Hidalgo
PublisherIOS Press
Pages365-372
Number of pages8
Volume319
ISBN (Electronic)9781643680149
DOIs
Publication statusPublished - 02 Oct 2019
Externally publishedYes
Event22nd International Conference of the Catalan Association for Artificial Intelligence, CCIA 2019 - Mallorca, Spain
Duration: 23 Oct 201925 Oct 2019

Publication series

NameFrontiers in Artificial Intelligence and Applications
Volume319
ISSN (Print)0922-6389

Conference

Conference22nd International Conference of the Catalan Association for Artificial Intelligence, CCIA 2019
CountrySpain
CityMallorca
Period23/10/201925/10/2019

Keywords

  • Breast cancer
  • Deep learning
  • Mammography
  • Mass detection

ASJC Scopus subject areas

  • Artificial Intelligence

Fingerprint Dive into the research topics of 'Mass Detection in Mammograms Using a Robust Deep Learning Model'. Together they form a unique fingerprint.

Cite this