Identification of Breast Cancer Using the Extreme Learning Machine Assisted by Firefly Algorithm

Year 2019, Issue: 17, 637 - 644, 31.12.2019

Deniz Üstün

https://doi.org/10.31590/ejosat.623816

Abstract

The Breast cancer is the
second cancer type which causes death of women. The premature detection of
cancer and the suitable treatment applied to cancer cells can reduce the deadly
risk. The medical doctors can make faults in diagnosis of the cancer disease.
The performance of artificial intelligence methods (AIMs) containing increased
thanks to rapid improvements in the technologies of the computer hardware. AIMs
can be used regarding the enhancement of diagnostic accuracy. Standard
Gradient–Based back propagation artificial neural networks (BP–ANN) has been
commonly utilized in the diagnosis of the breast cancer disease. Even though
BP–ANN are good performance in diagnosis of cancer disease, it has some
limitations such as possible to be trapped in local minima and long time in the
training process. In this study, the extreme learning machine assisted by
heuristic firefly algorithm (FF–ELM) is proposed for diagnoses of breast cancer
disease on the Breast Cancer Wisconsin Dataset. The diagnostic performance of
proposed FF–ELM was compared with the standard ELM and BP–ANN methods. The
results show that FF–ELM provides a meaningful enhancement regarding the
classification performance and it can be used as a powerful technique for the
medical problems.

Keywords

Extreme Learning Machine, Firefly Algorithm, Breast Cancer, Medical Decision Support Systems

Ateş Böceği Algoritması Destekli Aşırı Öğrenme Makinesi ile Göğüs Kanseri Veri Kümelerinin Sınıflandırılması

Abstract

Göğüs kanseri hastalığı,
kadınların ölümüne neden olan ikinci kanser türüdür. Kanser hastalığının erken
teşhisi ve kanser hücrelerine uygulanan uygun ve doğru tedavi hastalığın ölümcül
riskini azaltabilir. Tıp doktorları, kanser hastalığının teşhisinde zaman,
zaman hata yapabilmektedirler. Yapay zeka tekniklerinin (YZT) performansı,
bilgisayar donanım teknolojilerindeki hızlı gelişmeler sayesinde artmıştır.
Buna bağlı olarak, kanser hastalığının tanı doğruluğunun arttırılması ile
ilgili olarak YZT’ler kullanılabilir. Standart Eğime Dayalı Geri Yayılım Yapay
Sinir Ağları (GY–YZT), göğüs kanseri hastalığının tanısında yaygın olarak
kullanılmaktadır. GY–YZT, kanser hastalığının teşhisinde iyi bir performans
sergilese de, yerel minimum ve eğitim sürecinde uzun süre takılma gibi bazı
sınırlamaları vardır. Bu çalışmada, Göğüs Kanseri Wisconsin veri kümesinde göğüs
kanseri hastalığının teşhisi için, sezgisel ateş böceği algoritması tarafından
desteklenen aşırı öğrenme  makinesi
(AB–AÖM) önerilmiştir. Önerilen AB–AÖM’nin hastalık tanı üzerindeki performansı
standart AÖM ve GY–ANN yöntemleriyle karşılaştırıldı. Sonuçlar, AB–AÖM’nin
sınıflandırma performansıyla ilgili anlamlı bir gelişme sağladığını ve tıbbi
problemler için güçlü bir teknik olarak kullanılabileceğini göstermektedir.

Keywords

Aşırı Öğrenme Makineleri, Ateş Böceği Algoritması, Göğüs Kanseri, Tıbbi Karar Destek Sistemleri

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