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Journal of Applied Sciences
  Year: 2008 | Volume: 8 | Issue: 7 | Page No.: 1149-1158
DOI: 10.3923/jas.2008.1149.1158
 
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Classification of the Medical Images by the Kohonen Network SOM and LVQ

Z. Chalabi, N. Berrached, N. Kharchouche, Y. Ghellemallah , M. Mansour and H. Mouhadjer

Abstract:
This study fits within the framework of the diagnosis assistance and deals with the MR brain image types. To highlight the possibility of cerebral pathology such as tumours, one of the newest techniques of pattern recognition which exploits SOM (Self Organization Map) and LVQ (Learning Vector Quantization) algorithms of Kohonen is proposed. A short outline on these algorithms is brought back. Pre-processing adopted method is presented describing the training basis construction. Three classification approaches are carried out, comparative studies are conducted. The algorithm`s proprieties are verified according to the iteration number and the maps size. The classification quality is expressed via two parameters: the quantization error (QE%) and the good classification rate (CR%). Five pathological images and a healthy one are tested. The obtained results are in accordance with those of the results presented in the referred bibliographic.
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How to cite this article:

Z. Chalabi, N. Berrached, N. Kharchouche, Y. Ghellemallah , M. Mansour and H. Mouhadjer , 2008. Classification of the Medical Images by the Kohonen Network SOM and LVQ. Journal of Applied Sciences, 8: 1149-1158.

DOI: 10.3923/jas.2008.1149.1158

URL: https://scialert.net/abstract/?doi=jas.2008.1149.1158

COMMENTS
05 May, 2019
ankit patel:
sir i want python code of image classification using kohonen self organizing maps and report
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