# Brain Tumor Detection with Deep Learning Methods' Classifier Optimization Using Medical Images
## 2 Related Studies
Before and after this research model's data augmentation using the VGG-19 algorithm, some optimization techniques were applied and high success rates were achieved [12]. In the literature, it has been seen that the VGG algorithm is frequently used in brain tumor detection and classification studies. The biggest reason for this is that the algorithm gives successful results in similar datasets. In another study, MR images of three different tumor types such as glioma, meningioma and pituitary were classified using ResNet architecture. In order to obtain a better result in the research, changes were made in the layers, and the number of layers was increased. During the training, the Fighare MRI dataset consisting of 3064 T1-weighted MR images of 233 patients with three different tumor types containing, respectively, 1426, 708 and 930 images was used. The accuracy rate obtained as a result of the research was 98.67% [13]. Another study aimed to improve the detection and precise localization of brain cancer to improve the prognosis and treatment outcomes of patients by leveraging the information provided by brain medical images. Here, 300 brain images were analyzed using the YOLO model and a success percentage of 0.94 was achieved [14]. In research conducted with the transfer learning method, contrast stretching and histogram equalization methods were applied to the input images using the pre-trained ResNet50 architecture, and the success rates were compared in terms of precision and sensitivity. Here, the ResNet50 method achieved a very high success rate of 99.15%, with contrast stretching for the classification process [15]. In another study using three different convolutional neural networks, brain tumor types such as pituitary, glioma and meningioma have been classified via VGGNet, GoogleNet and AlexNet. In the research, the VGG16 architecture achieved a success rate of 98.69% in terms of classification and detection [16]. While searching the literature, it was observed that some studies did not apply any optimization in CNN algorithms, and only machine learning methods were used for classification. Brain tumor segmentation was also performed using the BRaTS 2020 dataset. As a result, an 86% similarity rate and 80% sensitivity percentage were obtained [17]. In addition, using the BraTS 2018 dataset, a U-Net-based model was developed to classify the tumor region using colored pixel label segmentation. As a result of this classification 98% success rate was achieved [18]. In the research, in which a random forest classifier-based system was proposed by dividing the brain images into two classes, first of all, an adaptive median filter was applied to the MR images in the preprocessing part. The goal here was to preserve the edge pixels on the image edges. Then, feature extraction was applied to determine the tumor region. A weighted voting technique is used to distinguish between tumor and non-tumor regions [19]. The study, employing contrast-enhanced MRI, achieved a 93% sensitivity, 82% specificity, and 87% accuracy using classical machine learning techniques. It aimed to predict the 1p/19q co-deletion status in 159 lower-grade gliomas (LGG). This was done by analyzing post-contrast MRI images with convolutional neural network (CNN) algorithms, utilizing a dataset specifically for LGG 1p/19q [20]. Table 1 shows the data sets used by similar studies and the accuracy rates found.