researcher Researcher 2717-9494 2717-9494 Ankara Bilim University Software Engineering (Other) Yazılım Mühendisliği (Diğer) Özellik Seçimine Dayalı Güçlendirme Algoritmaları Kullanılarak Beyin Tümörünün Tespiti Detection of Brain Tumor using Boosting Algorithms based on Feature Selection https://orcid.org/0000-0003-1567-2216 Özer Ezgi ISTINYE UNIVERSITY 12 31 2024 04 02 130 140 05 19 2024 07 23 2024 Copyright © 2013, Researcher 2013 Researcher

Beyin tümörleri en yaygın ölüm nedenlerinden biridir. Beyin tümörlerinin erken ve doğru tanımlanması etkili tedavi için kritik öneme sahiptir. Beyin tümörü tespitinde geleneksel yöntemler yerine yapay zeka tabanlı yazılım programlarının kullanılması daha doğru sonuçlar sağlayabilir. Özellikle son zamanlarda tıbbi görüntülerin işlenmesine dayalı olarak hastalıkların tespitine yönelik birçok çalışma yapılmaktadır. Bu çalışmada, üç farklı özellik seçme algoritmasına (ki-kare testleri kullanılarak sınıflandırma için tek değişkenli özellik sıralaması (f-chi2), ReliefF algoritması kullanılarak özelliklerin önem sıralaması (f-Relief), sıralama özellikleri) dayalı yeni bir hibrit algoritma önerilmiştir. sınıflandırma için minimum artıklık maksimum alaka algoritması (f-mRMR) kullanılarak klasik ve topluluk öğrenme, sırasıyla farklı çekirdek yapılarına sahip destek vektör makinesine (SVM) ve güçlendirme yöntemleriyle topluluk öğrenmeye (EL) dayalı olarak beyni tespit etmek için gerçekleştirildi. Aşırı uyumu önlemek için manyetik rezonans görüntüleme (MRI) özelliklerini kullanan tümör. Analiz sonuçları, önerilen hibrit yöntemle beyin tümörlerinin tespitinde topluluk bazlı sınıflandırıcıda %100 doğruluk puanına ulaşıldığını göstermektedir. Tümörlerin tespitine yönelik yenilik olarak, karmaşık ağ problemlerinde boyutun azaltılmasına ve dolayısıyla karmaşıklığın azaltılmasına yardımcı olacak istatistik tabanlı özellik seçim yöntemleri önerilmektedir. Önerilen yöntem, gelecekteki çalışmalarda veri boyutunun azaltılmasına yardımcı olabilecek bir özellik seçim algoritması önermektedir.

Brain tumors are one of the most common causes of death. An early and correct identification of brain tumors is critical for effective therapy. Using artificial intelligence-based software programs instead of traditional methods can provide more accurate results in brain tumor detection. Especially recently, there have been many studies in the detection of diseases based on the processing of medical images. In this study, a novel hybrid algorithm was proposed based on three different feature selection algorithms (univariate feature ranking for classification using chi-square tests (f-chi2), rank the importance of features using ReliefF algorithm (f-Relief), rank features for classification using minimum redundancy maximum relevance algorithm (f-mRMR), and the classic and ensemble learning, respectively based on support vector machine (SVM) with different kernel structures and ensemble learning (EL) with boosting methods, were performed to detect the brain tumor using magnetic resonance imaging (MRI) features. K-fold is used to prevent overfitting. Analysis results show that a 100% accuracy score was achieved in the ensemble-based classifier in the detection of brain tumors with the proposed hybrid method. As a novelty for detecting the tumors, statistics-based feature selection methods are proposed, to help reduce the size and thus reduce complexity in complex network problems. The proposed method suggests a feature selection algorithm that can help reduce the data size in future studies.

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