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Journals / 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
Pages
1605–1611
Publication Date
2022-10-18
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

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 Key Takeaways

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Objective & Hypothesis:Abstract : This study aims to produce a diagnosis system for breast masses related to breast cancer.
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Key Findings & Data: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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Conclusion & Impact: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.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

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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Cite This Article

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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References (8)

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