Segmentation of MR and CT images using a hybrid neural network trained by genetic algorithms

Dokur Z.

NEURAL PROCESSING LETTERS, cilt.16, sa.3, ss.211-225, 2002 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 16 Sayı: 3
  • Basım Tarihi: 2002
  • Doi Numarası: 10.1023/a:1021769530941
  • Dergi Adı: NEURAL PROCESSING LETTERS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.211-225
  • İstanbul Teknik Üniversitesi Adresli: Evet

Özet

A novel hybrid neural network trained by the genetic algorithms is presented. Genetic algorithms are used to improve the neural net's classification performance while minimizing the number of nodes. Each node of the network forms a closed region in the input space. The closed regions, which are formed by the nodes, intersect each other. The performance of the proposed hybrid neural network is compared with the multilayer perceptron, and the restricted Coulomb energy network for the segmentation of MR and CT head images. Experimental results show that the proposed neural network gives the best classification performance with a small number of nodes in short training times.