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PROSTAT HÜCRE ÇEKİRDEKLERİNİN SINIFLANDIRILMASINDA İSTATİSTİKSEL YÖNTEMLERİN VE YAPAY SİNİR AĞLARININ BAŞARIMI

PERFORMANCE OF STATISTICAL METHODS AND ARTIFICIAL NEURAL NETWORKS FOR CLASSIFICATION OF PROSTATE CELL NUCLEI

Journal Name:

  • Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi

Publication Year:

  • 2005

Key Words:

Keywords (Original Language):

Author NameUniversity of AuthorFaculty of Author
Abstract (2. Language): 
In this study, performances of different classifiers were analyzed for pathological data. Gauss Markov random field, Fourier entropy, and wavelet mean deviation features were calculated for 80 normal and 80 cancerous prostate cell nuclei and a common feature set was created from the ones having the discrimination power. Neural networks, K-nearest neighbor, nearest mean, and linear discriminant classifiers were used for classification. In this stage backpropagation neural networks having 3 to 15 hidden layer nodes were trained and tested. Highest classification rate (85.5%) was achieved by the nearest mean classifier.
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Abstract (Original Language): 
Bu çalışmada, patolojik verilere uygulanan farklı sınıflandırıcıların başarımları analiz edilmiştir. 80 normal ve 80 kanserli prostat hücre çekirdek imgesinden, Gauss Markov rassal alanlar, Fourier entropi ve dalgacık dönüşümü ortalama sapma öznitelik vektörleri elde edilmiş ve ayrım gücü olanlardan ortak bir öznitelik vektörü oluşturulmuştur. Sınıflandırma için yapay sinir ağları, k-en yakın komşu, en yakın merkez ve doğrusal ayırtaç yöntemleri kullanılmıştır. Bu aşamada, 3-15 arası ara katman düğümüne sahip geri yayılımlı yapay sinir ağı, sınıflandırma amacı ile eğitilip test edilmiştir. En yüksek genel başarım oranını %85.5 ile en yakın merkez sınıflandırıcısı sağlamıştır.
FULL TEXT (PDF): 
PDF icon arastirmax-prostat-hucre-cekirdeklerinin-siniflandirilmasinda-istatistiksel-yontemlerin-yapay-sinir-aglarinin-basarimi.pdf
  • 3
31-36
  • Turkish

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