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Dergiler / Istanbul University Journal of Electrical and Electronics Engineering / 2013 / Cilt: 13 - Sayı: 1

Classification of breast masses using anfis-based fuzzy algorithms: a comparative study

Pelin GÖRGEL
Ainura TURUSBEKOVA
Ahmet SERTBAS
Sayfa
1605–1611
Yayın Tarihi
2022-10-18
DOI
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

It is a fuzzy based comperative study of malignant-benign classification for breast masses which has the accuracy of 74.36% with k-means and 93.75% with ANFIS based fuzzy c-means and subtractive clustering.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Abstract : This study aims to produce a diagnosis system for breast masses related to breast cancer.
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Temel Bulgular & Veri:The dataset consisting of 60 digital mammograms is acquired from Istanbul University Faculty of Medicine Hospital. 78 masses in the mammograms are extracted manually for this study by the experts.
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Sonuç & Katkı:It is a fuzzy based comperative study of malignant-benign classification for breast masses which has the accuracy of 74.36% with k-means and 93.75% with ANFIS based fuzzy c-means and subtractive clustering.
🔬Tür:Araştırma Makalesi
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Alan Parametreleri

Abstract

Abstract : This study aims to produce a diagnosis system for breast masses related to breast cancer. The dataset consisting of 60 digital mammograms is acquired from Istanbul University Faculty of Medicine Hospital. 78 masses in the mammograms are extracted manually for this study by the experts. It is a fuzzy based comperative study of malignant-benign classification for breast masses which has the accuracy of 74.36% with k-means and 93.75% with ANFIS based fuzzy c-means and subtractive clustering.

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Bu Makaleye Atıf Yap

GÖRGEL, P., TURUSBEKOVA, A., SERTBAS, A. (2022). Classification of breast masses using anfis-based fuzzy algorithms: a comparative study. Istanbul University Journal of Electrical and Electronics Engineering, Cilt: 13 - Sayı: 1, 1605–1611. https://ajindex.com/articles/543668

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Kaynakça (8)

  1. In-Sung J., Devinder T. and Wang G.N. Neural Network Based Algorithms for diagnosis and classification of breast canser tumor. Department of Industrial and Information Engineerin, Ajou University, South Korea, 2011
  2. Gorgel P., "Cancer Region diagnosis of 2-dimensional mammographic data using image processing techniques", İstanbul University, The Institute of Sciences, Computer Engineering Department, PhD thesis, 2011
  3. DUNN, J.C., 1974, A Fuzyy Relative of ISODATA Process and Its Use in Detecting Compact, Well Separated Clusters, Journ., Cybern., 3, 95-104
  4. BEZDEK, J.C., 1981, "Pattern Recognition with Fuzzy Objective Function Algorithms", Plenum Press, New York
  5. Hongxing, L. et al. 2001. Fuzzy Neural Intelligent System, Mathematical Foundation and the Application in Engineering. CRC Press LLC
  6. SUGENO, M., 1977, "Fuzzy measures and fuzzy integrals: a survey," (M.M. GUPTA, G. N. SARIDIS, and B.R. GAINES, editors) Fuzzy Automata and Decision Processes, pp. 89-102, North-Holland, NY
  7. JANG, J.-S. R. and C.-T. SUN, 1997, "Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence", Prentice Hall
  8. Geisser, Seymour (1993). Predictive Inference. New York, NY: Chapman and Hall. ISBN 0412034719.