Oksuz C., Gullu M.K.2023-05-092023-05-092020-10-059781728172064https://hdl.handle.net/20.500.12597/13362In this study, an optimal deep feature set is determined for the classification of brain tumor tissue types Glioma, Meningioma and Pituitary. It is a good alternative to use a pre-trained network that is trained with millions of data in areas such as medical imaging where the amount of available data is not sufficient to train a CNN. Another method is to train the last layers of the network known as fine-tuning by configuring the problem at hand. In this study, AlexNet is used. Then, an SVM model is trained using features extracted from pre-trained and fine-tuned networks. Experimental results show that the 3D feature vectors extracted from the fine-tuned network yield superior results in a different test set, compared to the feature vectors extracted directly from the pre-trained one and increase the generalization ability of the model.falseBrain tumor classification | deep feature extraction | deep feature selection | transfer learningConference Paper10.1109/SIU49456.2020.930210810.1109/SIU49456.2020.93021082-s2.0-85100298339