# Brain Tumor Detection with Deep Learning Methods' Classifier Optimization Using Medical Images
## 3 Material and Method  
### Datasets  
The brain is one of the most complex and important structures in the human body. It consists of more than 50 billion nerves connected by millions of connections and a special field working together. Although the brain is the control center of the whole body, it organizes the coordinated work of the heart, lungs, blood vessels and all other organs. All of our senses are connected to the brain [21].  
Brain tumors are a deadly disease that develops, especially in adults, with the formation and proliferation of abnormal cells. It is usually caused by abnormal growth of the central nervous system or brain cells. Brain tumors are divided into two groups as primary and secondary brain tumors, and the types of tumors in these two groups are categorized separately. Knowing and classifying brain tumors according to group is of vital importance for patients. Primary brain tumors are tumors originating from a cell or tissue in the brain. These can be categorized among themselves as benign and malignant. Benign tumors occur in a single region and grow relatively slowly. If the correct procedure is performed with the surgical operation, these tumors most likely do not recur. It is very  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**References** & **Model** & **Dataset Quantity** & **Accuracy** \\ \hline
[14] & YOLO & 300 & 0.94 \\
[9] & Hybrid Model & 500 & 0.99 \\
[4] & VGG-16 & 253 & 0.98 \\
[15] & ResNet50 & 2100 & 0.991 \\
[5] & DenseNet121 & 3064 & 0.986 \\
[18] & U-Net & BraTS 2018 & 0.98 \\
[17] & CNN & BraTS 2020 & 0.80 \\
[6] & Inception-V3, DenseNet & 3064 & 0.993 \\
[13] & ResNet50 & 3064 & 0.986 \\
[16] & VGG, GoogleNet, AlexNet & 3064 & 0.986 \\
[13] & VGG16-VGG19 & 233 & 0.948 \\
[12] & VGG-19 & 121 & 0.873 \\
[10] & CNN & 3580 & 0.961 \\
[20] & CNN & 159 & 0.870 \\
[19] & CNN & 985 & 0.85 \\
[7] & CNN & 497 & 0.842 \\
[11] & Neuro-fuzzy & 20 & - \\
[8] & KNN, SVM & 51 & 0.80 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of accuracy rates of brain tumor studies.  
important to completely remove the tumor and clean the area. Malignant tumors in the brain and spinal cord spread and multiply rapidly. Malignant tumors are classified as secondary brain tumors, and rarely start elsewhere in the body and spread to the brain. These tumors are graded between 1 and 4 according to their growth rate. Considering the grading criteria, grades 1 and 2 are considered benign, while grades 3 and 4 are considered malignant [22].  
In this research, the authors used the \"Brain Tumor MRI dataset\" whose MR images were published as open access by [PERSON] on the Kaggle platform. This dataset is a combination of three different datasets (Figshare, SARTAJ, Br35H). This dataset contains 7022 brain MRI images in total, consisting of 4 different classes. In the figures below, images are given of four different classes and also the numbers of these classes [23].  
As seen in Figures 1 and 2, tumorous tissues are marked with green dots. However, this is not always possible, because not all tumors are so big that they can be seen. Missed cases can sometimes become life-threatening in a very short time. The underlying principle of this research is to minimize human error and utilize artificial intelligence. So the dataset consists of 4 classes: glioma, malignant, pituitary and no tumor. The distribution of the classes is as follows: gliomas (1321 images), malignant (1339 images), pituitary tumors (1456 images) and normal tumor cells (1595 images). The original dataset is divided into 5711 training data and 1311 test data images. However, for evaluation, 40% of the test set is allocated to the evaluation set and the remaining 60% to the training set.  
Figure 3 shows the flow chart of the model. Here, All classification and CNN algorithms are presented.  
Figure 1: Glioma and malignant brain tumor.  
Figure 2: Pituitary and no-tumor (healthy) brain tumor.