The brain cancer treatment process depends on the physician's experience and knowledge. For this reason, using an automated tumor detection system is extremely important to aid radiologists and physicians to detect brain tumors. The proposed method has three stages, which are preprocessing, the extreme learning machine local receptive fields (ELM-LRF) based tumor classification, and image processing based tumor region extraction. At first, nonlocal means and local smoothing methods were used to remove possible noises. In the second stage, cranial magnetic resonance (MR) images were classified as benign or malignant by using ELM-LRF. In the third stage, the tumors were segmented. The purpose of the study was using only cranial MR images, which have a mass, in order to save the physician's time. In the experimental studies the classification accuracy of cranial MR images is 97.18%. Evaluated results showed that the proposed method's performance was better than the other recent studies in the literature. Experimental results also proved that the proposed method is effective and can be used in computer aided brain tumor detection.

Magnetic Resonance Imaging (MRI) is a noninvasive medical testing procedure that can help physicians to examine internal body structures and diagnose a variety of disorders, such as tumors. MRI has some advantages over other imaging methods: mainly that there is no risk of being exposed to radiation. As a result of this many researchers from the community of computer vision and machine learning are interested in classifying or segmenting MR images to help physicians perform more detailed investigations and an automatic system for brain tumor detection and classification was proposed. Firstly, brain MR images are preprocessed by using a 5x5 Gaussian filter. Secondly, deep feature extraction was performed by using Alex Net and VGG16 models of pre-trained Convolutional Neural Network (CNN). The obtained feature vectors are combined. These feature vectors were used for MR images classification by Extreme Learning Machines (ELM) classifier. The performances of the proposed methods have been evaluated on three different data sets. Performance parameters used to assess the results are; accuracy, sensitivity, selectivity and Jaccard's similarity index for tumor detection. The experimental results showed that the proposed system is superior in detecting and classifying brain tumors when compared with other systems.

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Highlights CNN model design and feature extraction for emotion recognition problem  Use of the publicly available Fer+ facial expression dataset  Feature selection with Binary Particle Swarm Optimization (BPSO) algorithm  Classification of features with Support Vector Machine (SVM)

 Farklı derin öğrenme modellerinin sinirsel stil aktarım performansları karşılaştırılmıştır. / Neural style transfer performance of different deep learning models was compared.  Farklı optimizasyon algoritmaları ile derin öğrenme modellerinin performansları karşılaştırılmıştır. / The performances of different optimization algorithms and deep learning models were compared.