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 language | English |
---|---|
Title of host publication | Proceedings - UKSim-AMSS 10th European Modelling Symposium on Computer Modelling and Simulation EMS 2016 |
Editors | David Al-Dabass, Valentina Colla, Marco Vannuci, Athanasios Pantelous |
Place of Publication | Piscataway, NJ |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Pages | 27-32 |
Number of pages | 6 |
ISBN (Electronic) | 9781509049714 |
ISBN (Print) | 9781509049721 |
DOIs | |
Publication status | Published - 2016 |
Event | 10th European Modelling Symposium on Computer Modelling and Simulation, UKSim-AMSS 2016 - Pisa, Italy Duration: 28 Nov 2016 → 30 Nov 2016 |
Other
Other | 10th European Modelling Symposium on Computer Modelling and Simulation, UKSim-AMSS 2016 |
---|---|
Country/Territory | Italy |
City | Pisa |
Period | 28/11/16 → 30/11/16 |
Keywords
- Confusion Matrix
- Neural Network
- Receiving Operating Curve
- Support Vector Machine