Bayes optimizasyon tabanlı SVM sınıflandırıcı ve ince-ayar tabanlı derin özelliklerinin kombinasyonu kullanılarak Beyin Tümörü Tespiti

Year 2021, Issue: 27, 251 - 258, 30.11.2021

Muammer Türkoğlu

https://doi.org/10.31590/ejosat.963609

Cited By: 2

Abstract

En sık görülen kanser türlerinden biri olan beyin tümörü ölümcül bir hastalıktır. Bu nedenle bu hastalığın doğru teşhisi ve tümörün tipinin belirlenmesi erken tedavi açısından büyük önem taşımaktadır. Bu bağlamda, son zamanlarda beyin tümörü sınıflandırmasında yaşanan problemler için derin öğrenmeye dayalı otomatik sistemlerin geliştirilmesine yönelik araştırmalar ve ilgi artmıştır. Bu çalışmada, beyin tümörlerinin sınıflandırılması için Bayesian optimizasyon tabanlı Destek Vektör Makinesi (DVM) sınıflandırıcısı ve Evrişimsel Sinir Ağı (ESA) tabanlı derin öznitelikler topluluğuna dayalı benzersiz bir tasarım önerilmiştir. Bu modelde öncelikle beyin MRI görüntüleri iyileştirildi. İkinci olarak, derin öznitelikler, önceden eğitilmiş ESA tabanlı derin mimariler kullanılarak çıkartıldı ve ardından birleştirildi. Daha sonra, MrMr algoritması ile bu derin özelliklerden etkili ve ayırt edici özellikler seçildi. Son olarak, bu özellikler, Bayes optimizasyon algoritmasına dayalı DVM sınıflandırıcısının eğitiminde kullanıldı. Önerilen sistemi test etmek için, meningiom, glioma ve hipofiz gibi beyin tümörü görüntülerini içeren Figshare adlı bir veri seti kullanıldı. Deneysel çalışmalarda, önerilen modelin doğruluk skoru diğer çalışmalardan daha başarılı olduğu gözlemlenmiştir.

Keywords

Beyin Tümör Sınıflandırımlası, Özellik Seçimi, Evrişimsel Sinir Ağları, Destek Vektör Makinesi, Bayes Optimizasyon

Brain Tumor Detection using a combination of Bayesian optimization based SVM classifier and fine-tuned based deep features

Abstract

Brain tumor, one of the most common types of cancer, is a fatal disease. Therefore, accurate diagnosis of this disease and determining the type of tumor are of great importance in terms of early treatment. In this context, research, and interest in the development of automatic systems for the problems experienced in brain tumor classification, based on deep learning, have increased recently. In this study, a unique framework is proposed, which is based on Bayesian optimization-based Support Vector Machine (SVM) classifier and Convolutional Neural Network (CNN) based deep features ensemble, for the classification of brain tumors. In this model, brain MRI images are first improved. Second, the deep features are extracted using pre-trained CNN-based deep architectures and then combined. Later, effective, and distinctive features are selected from these deep features with the MrMr algorithm. Finally, these features are used in the training of the SVM classifier based on the Bayesian optimization algorithm. A dataset named Figshare, containing brain tumor images such as meningioma, glioma, and pituitary, is used to test the proposed system. In the experimental studies, the accuracy score of the model proposed was observed to be more successful than that of the other studies.

Keywords

Brain Tumor Classification, Feature selection, Convolutional Neural Network, Support Vector Machine, Bayesian optimization

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