# Brain Tumor Detection with Deep Learning Methods' Classifier Optimization Using Medical Images
## 2 Related Studies  
Computer vision is software science that focuses on pictorial images, clippings from videos, object identification and understanding. The main purpose of image processing, which is a product of artificial intelligence, is to create systems that can copy human abilities and learn them on their own. In addition, image processing applications use machine and deep learning architectures to imitate the visual processing that occurs in living things. Many image processing techniques are used. Medical image processing is a widely used method for rapid diagnosis. When the studies were examined, it was seen that studies were carried out to diagnose tumors of different types and sizes using magnetic resonance images. In a sample research study, the aim was to classify different brain tumors (pituitary, glioma and meningioma tumor) using CNN algorithms on MR images and to determine the importance of brain sections such as coronal, axial and sagittal in classification. On the same topic, researchers have proposed a new model, and this model is derived from the DenseNet algorithm. The results were classified with machine learning algorithms and high success rates were achieved [5]. In a study, VGG architecture is preferred because it is easy to understand. The results obtained from 253 brain MRI images, 155 of which had tumors, showed that VGG achieved a 98% success rate [4]. In a similar study, images of three different brain tumors were used to extract sub-layer images that are different from medical images using pre-trained models. Here, feature extraction and merging methods are used while solving the problem. Inception-v3 and DenseNet architectures were used in this problem, and success rates of 99.34% and 99.51% were achieved, respectively, from these two models [6]. In another study, magnetic resonance images were used for brain tumor detection. In this research, a rule-based detection system was introduced and morphological features were utilized. Respectively, for 497 brain MRI images, preprocessing, segmentation, tumor region detection and tumor detection stages were followed. Here, a success rate of 84.26% was achieved [7]. In addition, the [PERSON] study group proposed transfer learning for multi-class brain tumor classification. For this purpose, the AlexNet, VGG-16 and VGG-19 ESA models were used. In the experimental studies, respectively, the AlexNet, VGG-16 and VGG-19 models achieved 89.95%, 94.65% and 94.82% accuracy rates. A statistical study to classify benign and malignant brain tumors compared textural characteristics. Here, the nearest-neighbor algorithm was used and a classification accuracy of 80% was achieved [8]. In a segmentation study developed in addition to CNN structures, a diagnosis study was conducted using MR images. In this study, a thresholding technique was applied using a search algorithm. Morphological operations and connected component analysis were used to reduce the noise in the images and to identify brain tumors at a higher rate. The results obtained were compared with CNN algorithms and high success was achieved [9]. In their research with two different datasets, ref. [10] aimed to see the success rates of classification by applying different labels. A CNN-based deep learning algorithm was tested to classify 3580 open access brain MRI images. An accuracy rate of 96.13% was achieved in the study using first and fourth stage tumors. In a study using a neuro-fuzzy inference system, brain MR images were divided into component tissues, and diseased tissues such as spinal fluid, edema and tumors were separated. Unlike similar studies in the literature, the skull was separated and only brain tissue was evaluated. The statistical features obtained from the system were used and the results were compared with the segmented tissue areas and evaluated with the membrane index. As a result of the research, it was shown that the neuro subtraction system gave very successful results in the segmentation process of MR images [11]. VGG algorithm was used to classify brain tumor images.