RADYOMİK ÖZELLİKLER VE MAKİNE ÖĞRENMESİ TEKNİKLERİYLE MEME TÜMÖRLERİNİN SINIFLANDIRILMASI

Asuman Kaplan; Esra Kavadar; Mehmet Ali Altuncu

doi:10.17780/ksujes.1512476

TR EN

RADYOMİK ÖZELLİKLER VE MAKİNE ÖĞRENMESİ TEKNİKLERİYLE MEME TÜMÖRLERİNİN SINIFLANDIRILMASI

Öz

Meme kanseri, dünya genelinde kadınlar arasında en sık görülen kanser türüdür ve erken teşhis, tedavi başarısını önemli ölçüde artırmaktadır. Bu çalışmada, meme ultrason görüntülerinden iyi huylu ve kötü huylu tümörleri sınıflandırmak amacıyla radyomik özellikler ve makine öğrenmesi teknikleri kullanılmıştır. Çalışmada, halka açık BUSI veri seti kullanılmıştır. Sadece iyi huylu ve kötü huylu olarak etiketlenmiş görüntüler sınıflandırmada kullanılmış olup, normal etiketli görüntüler çalışmaya dahil edilmemiştir. Bu yaklaşım, modelin iki sınıf arasındaki ayrımı en yüksek doğrulukla yapmasına odaklanmıştır. Veri setindeki dengesizlik, kötü huylu tümörlerin görüntülerinin y ekseninde aynalanarak artırılmasıyla giderilmiştir. PyRadiomics kütüphanesi ile çıkarılan 123 radyomik özellik arasından, özellik önem skoru ve korelasyon matrisi kullanılarak en önemli 40 özellik seçilmiştir. Sınıflandırma aşamasında XGBoost, Gradient Boosting, AdaBoost, SVM, Random Forest ve Decision Tree algoritmaları uygulanmış, en yüksek doğruluk oranı (%98.13) Gradient Boosting algoritması ile elde edilmiştir.

Anahtar Kelimeler

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Details

Primary Language

Turkish

Subjects

Reinforcement Learning , Machine Vision

Journal Section

Research Article

Authors

Asuman Kaplan
0009-0004-9357-1773
Türkiye

Esra Kavadar
0009-0005-9548-7548
Türkiye

Mehmet Ali Altuncu ^*
0000-0002-2948-3937
Türkiye

Publication Date

March 3, 2025

Submission Date

July 8, 2024

Acceptance Date

November 7, 2024

Published in Issue

Year 1970 Volume: 28 Number: 1

DOI

https://doi.org/10.17780/ksujes.1512476

IZ

https://izlik.org/JA72UN59PA

Cite

RIS / Bibtex

APA

Kaplan, A., Kavadar, E., & Altuncu, M. A. (2025). RADYOMİK ÖZELLİKLER VE MAKİNE ÖĞRENMESİ TEKNİKLERİYLE MEME TÜMÖRLERİNİN SINIFLANDIRILMASI. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 28(1), 38-50. https://doi.org/10.17780/ksujes.1512476

Cited By

MULTILAYER ANALYSIS OF NICOTINE-INDUCED GENE EXPRESSION ALTERATIONS IN BREAST CANCER CELLS USING CLUSTERING AND SUPERVISED LEARNING METHODS

Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi

https://doi.org/10.17780/ksujes.1730962

CLASSIFICATION OF BREAST TUMORS USING RADIOMIC FEATURES AND MACHINE LEARNING TECHNIQUES

RADYOMİK ÖZELLİKLER VE MAKİNE ÖĞRENMESİ TEKNİKLERİYLE MEME TÜMÖRLERİNİN SINIFLANDIRILMASI

Öz

Anahtar Kelimeler

CLASSIFICATION OF BREAST TUMORS USING RADIOMIC FEATURES AND MACHINE LEARNING TECHNIQUES

Abstract

Keywords

References

Details

Primary Language

Subjects

Journal Section

Authors

Publication Date

Submission Date

Acceptance Date

Published in Issue

DOI

IZ

Cite

Cited By

MULTILAYER ANALYSIS OF NICOTINE-INDUCED GENE EXPRESSION ALTERATIONS IN BREAST CANCER CELLS USING CLUSTERING AND SUPERVISED LEARNING METHODS