You are here

Home » Content

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.
Bookmark/Search this post with
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

REFERENCES

References: 

Aldroubi A. (1996): “The Wavelet Transform: A Surfing Guide”. pp.3-36, Wavelets in
Medicine and Biology, Aldroubi, A., Unser, M., (ed.), CRC Press.
Bovik A. C., Clark M., Geisler W. S. (1990): “Multichannel Texture Analysis using Localizad
Spatial Filters”, IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 12, s. 55-73.
Chatterjee S. (1991): “Classification of Natural Textures using Gaussian Markov Random
Field Models”. pp.159-177, Markov Random Fields, Theory and Applications, Chellappa,
R., Jain, A., (ed.), Academic Press.
Du Buf J.M.H., Kardan M., Spann M. (1990): “Texture Feature Performance for Image
Segmentation”. Pattern Recognition, Vol. 23, No. 3/4, pp.291-309.
Duda R.O., Hart P.E. (1973): “Pattern Classification and Scene Analysis”. John Wiley &
Sons.
Haralick R. M. (1979): “Statistical and Structural Approaches to Texture”. Proc. of the IEEE,
Vol. 67, No. 5, pp.786-804.
Jain A.K., Duin R.P.W., Mao J. (2000): “Statistical Pattern Recognition: A Rewiew”. IEEE
Trans. Pattern Analysis and Machine Intelligence, Vol. 22, pp.4-37.
Jernigan M.E., D’Astous F. (1984): “Entropy-based Texture Analysis in the Spatial
Frequency Domain”. IEEE Tran. Patt. Anal. Machine Intel., Vol. 6, pp. 237-243.
Kil D.H., Shin F.B. (1996): “Pattern Recognition and Prediction with Applications to Signal
Characterization”. AIP Press.
Li S.Z. (1995): “Markov Random Field Modeling in Computer Vision”, Springer-Verlag.
Manjunath B.S., Chellappa R. (1991): “Unsupervised Texture Segmentation using Markov
Random Field Models”. IEEE Tran. Patt. Anal. Machine Intel., Vol. 13, pp.478-482.
Misiti M., Misiti Y., Oppenheim G., Poggi J.-M. (1996): “Wavelet Toolbox”. The
MathWorks Inc.
Schürmann J. (1996): “Pattern Classification: A Unified View of Statistical and Neural
Approaches”. John Wiley & Sons.
Van de Wouwer G., Scheunders P., Van Dyck D. (1999): “Statistical Texture
Characterization from Discrete Wavelet Representation”. IEEE Trans. On Image
Processing, Vol. 8, pp. 592-598.
Weszka J. S., Dyer C. R., Rozenfeld A. (1976): “A Comparative Study of Texture Measures
for Terrain Classification”. IEEE Trans. on Systems, Man, and Cybenetics, Vol. SMC-6,
No. 6, pp. 269-285.

Thank you for copying data from http://www.arastirmax.com