Özet:

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.

Anahtar Kelimeler: