Evaluation of Learning Approaches Based on Convolutional Neural Networks for Mammogram Classification

Roberto Arias, Fabián Narváez, Hugo Franco

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

Abstract

Mammography is still considered the best screening method for detection, diagnosis and follow-up of breast cancer. A correct classification of mammographic findings demands a high expertise level of the clinician observer. For this, different Computer-aided Diagnosis systems have been developed to support the diagnosis tasks and reduce the inter or intra-observer variability caused by the complex visual information contained in mammograms. However, the classification of some findings (masses, calcifications) is still a difficult task. This work presents a methodological approach to evaluate the performance of the training process for different convolutional neural network configurations of the VGG16 Convolutional Neural Network architecture, designed to perform mammographic classification. For doing that, the impact of different learning strategies (focal loss, to deal with highly unbalance datasets, gradient clipping and learning transfer) is evaluated. The proposed method was two-fold evaluated. First, the performance for classifying between normal and abnormal Regions of Interest (ROIs) extracted from the DDSM and CBIS-DDSM datasets was explored. After that, a multi-class problem was addressed, for which a set of 5-class was included according to well-known BI-RADS classification. The obtained results reported an average accuracy of 0.92 for the binary classification and a rate of accuracy of 0.85 for the 5-class classification (with 30 epochs), reducing the convergence time (23 and 30 epochs for both binary and multi-class classification tasks, respectively).

Original languageEnglish
Title of host publicationSmart Technologies, Systems and Applications - 1st International Conference, SmartTech-IC 2019, Proceedings
EditorsFabián R. Narváez, Diego F. Vallejo, Paulina A. Morillo, Julio R. Proaño
PublisherSpringer
Pages273-287
Number of pages15
ISBN (Print)9783030467845
DOIs
StatePublished - 1 Jan 2020
Event1st International Conference on Smart Technologies, Systems and Applications, SmartTech-IC 2019 - Quito, Ecuador
Duration: 2 Dec 20194 Dec 2019

Publication series

NameCommunications in Computer and Information Science
Volume1154 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference1st International Conference on Smart Technologies, Systems and Applications, SmartTech-IC 2019
Country/TerritoryEcuador
CityQuito
Period2/12/194/12/19

Keywords

  • Class imbalance
  • Convolutional Neural Network
  • Focal loss
  • Gradient clipping
  • Mammogram
  • Transfer learning

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