SavedAdd your journal

Journals / Avrupa Bilim ve Teknoloji Dergisi / 2019 / Cilt: 0 - Sayı: 17

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

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

Deniz ÜSTÜN
Pages
637–644
Publication Date
2022-10-11
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

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

The results show that FF–ELM provides a meaningful enhancement regardingthe classification performance and it can be used as a powerful technique for the medical problems.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The Breast cancer is the second cancer type which causes death of women.
📊
Key Findings & Data:Theperformance of artificial intelligence methods (AIMs) containing increased thanks to rapid improvements in the technologies of thecomputer hardware.
💡
Conclusion & Impact:The results show that FF–ELM provides a meaningful enhancement regardingthe classification performance and it can be used as a powerful technique for the medical problems.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

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

Özet

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.

🔖

Cite This Article

ÜSTÜN, D. (2022). Identification of Breast Cancer Using the Extreme Learning Machine Assisted by Firefly Algorithm. Avrupa Bilim ve Teknoloji Dergisi, Cilt: 0 - Sayı: 17, 637–644. https://ajindex.com/articles/458459

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (9)

  1. Subashini, T. S.; Ramalingam, V.; Palanivel, S., “Breast mass classification based on cytological patterns using RBFNN and SVM”, Expert Syst. Appl., 2009, 36 (3): 5284–5290
  2. The American Cancer Society, What is Breast Cancer
  3. Akay, M. F., “Support vector machines combined with feature selection for breast cancer diagnosis”, Expert Syst. Appl., 2009, 36(2), 3240–3247
  4. West, D.; Mangiameli, P.; Rampal, R.; West, V., “Ensemble strategies for a medical diagnostic decision support system: A breast cancer diagnosis application”, Eur. J. Oper. Res., 2005, 162(2), 532–551
  5. Brause, R. W., “Medical Analysis and Diagnosis by Neural Networks”, In Proceeding ISMDA’01 Proceedings of the Second International Symposium on Medical Data Analysis, Madrid, Spain, 1–13, 2001
  6. P. Kshirsagar; N. Rathod, “Artificial neural network”, International Journal of Computer Applications, 2012, NCRTC(2), 12–16
  7. N. Gupta, “Artificial neural network”, Network and Complex Systems, 2013, 3(1), 24 – 28
  8. Huang, G.-B.; Chen, L., “Convex incremental extreme learning machine.”, Neurocomputing, 2007,70(16), 3056-3062
  9. Huang, G.-B.; Chen, L., “Enhanced random search based incremental extreme learning machine.”, Neurocomputing, 2008, 71(16), 3460-3468. [10]Huang, G.-B.; Chen, L.; Siew, C. K., Universal approximation using incremental constructive feedforward networks with random hidden nodes., IEEE Trans. Neural Networks, 2006, 17(4), 879-892. [11]Liu, X.; Lin, S.; Fang, J.; Xu, Z., “Is extreme learning machine feasible? A theoretical assessment (Part I). IEEE Transactions on Neural Networks and Learning Systems, 2015, 26(1), 7-20. [12]Huang, G.-B.; Zhou, H.; Ding X., Zhang R., “Extreme learning machine for regression and multiclass classification”, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2012, 42(2), 513-529. [13]Suykens, J. A.; Vandewalle, J., “Least squares support vector machine classifiers. Neural processing letters”, 1999, 9(3), 293-300. [14]Cortes, C.; Vapnik, V., “Support vector machine”, Machine learning, 1995, 20(3), 273-297. [15]Abdolmaleki, P.; Buadu, L.D.; Naderimanesh, H., “Feature extraction and classification of breast cancer on dynamic magnetic resonance imaging using artificial neural network”, Elsevier, Cancer Letters, 2001, 171 (2), 183-191. [16]Fogel, D. B.; Wasson, E.C.; Boughton, E.M.; Porto, V.W., “A step toward computer assisted mammography using evolutionary programming and neural Networks”, Cancer Letters