Title :
The self-organizing maps of Kohonen in the medical classification
Author :
Zribi, Mehrez ; Boujelbene, Younes ; Abdelkafi, I. ; Feki, R.
Author_Institution :
Fac. of Econ. & Manage., Sfax Univ., Sfax, Tunisia
Abstract :
In Tunisia, breast cancer is the most common cancer among women; it presents the leading cause of female mortality in the age group 35 to 55 years. This paper uses a neural approach based on Kohonen self-organizing maps to perform a classification of tumors (benign and malignant) using a sample of Tunisian women. Empirical results demonstrate the relevance of the approach and show that neural networks are an important decision support technique for detecting the presence of cancerous tissue in the breast and the classification of tumors.
Keywords :
cancer; gynaecology; medical computing; pattern classification; self-organising feature maps; Kohonen self-organizing maps; Tunisian women; breast cancer; cancerous tissue; female mortality; medical classification; neural approach; tumors classification; Breast cancer; Classification algorithms; Self-organizing feature maps; Tumors; Vectors; Classification; Kohonen self-organizing maps; breast cancer;
Conference_Titel :
Sciences of Electronics, Technologies of Information and Telecommunications (SETIT), 2012 6th International Conference on
Conference_Location :
Sousse
Print_ISBN :
978-1-4673-1657-6
DOI :
10.1109/SETIT.2012.6482027