Breast image classification based on concatenated statistical, structural and textural features

Abdullah-Al-Nahid, Tariq M. Khan, Yinan Kong

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

Abstract

Breast cancer is the most prevalent form of cancer. Statistics show that breast cancer causes the second highest mortality in women worldwide and around two million new cases were diagnosed every year. Accurate classification of breast cancer has acquired high importance for proper diagnosis which can save doctors and physiologist time. The breast, which contains fatty tissue is more vulnerable to cancer. In this paper, we classify a set of breast images based on Fatty tissue and non-Fatty tissue using a concatenated statistical, structural and textural feature set. For the classification, we have used Support Vector Machine (SVM) and Neural Network (NN) techniques as a classifier tool. Investigation shows that concatenated statistical, structural and textural features provide better classification result.

Original languageEnglish
Title of host publicationProceedings - UKSim-AMSS 10th European Modelling Symposium on Computer Modelling and Simulation EMS 2016
EditorsDavid Al-Dabass, Valentina Colla, Marco Vannuci, Athanasios Pantelous
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages27-32
Number of pages6
ISBN (Electronic)9781509049714
ISBN (Print)9781509049721
DOIs
Publication statusPublished - 2016
Event10th European Modelling Symposium on Computer Modelling and Simulation, UKSim-AMSS 2016 - Pisa, Italy
Duration: 28 Nov 201630 Nov 2016

Other

Other10th European Modelling Symposium on Computer Modelling and Simulation, UKSim-AMSS 2016
CountryItaly
CityPisa
Period28/11/1630/11/16

Keywords

  • Confusion Matrix
  • Neural Network
  • Receiving Operating Curve
  • Support Vector Machine

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