# A Deep Analysis of Brain Tumor Detection from MR Images Using Deep Learning Networks
## 2 Related Works  
In this section we are studied previous researches works about brain tumor detection using ML models. In Table 1 we have summarized some previous works. [PERSON] et al. [17] proposed a deep educational model using an MRI dataset for brain tumor detection. In addition to the deep educational model, they applied four other transfer learning models: VGG16, MobileNet, ResNet-50, and Inception V3. They used a dataset of 10,000 MR images with a 200 \(\times\) 200 pixel resolution to evaluate their models. The dataset was divided into two categories with 5000 images each: brain tumors and non-brain tumors. Their proposed model, the deep educational model, performed better; the training accuracy was 100%, and the test accuracy was 98%. [PERSON] et al. [34] introduced a DCNN model using an MRI dataset for detecting brain tumors. In their proposal, a lightweight model with a few convolutions, max-pooling, and iterations was used. The researchers also analyzed VGG16, VGG19, and CNN-SVM, Glioma (934), meningioma (945), no tumor (606), and pituitary(909) were the four subcategories of the 3394 MR images. The suggested model achieved an overall accuracy of 97.72%, a detection rate of 99% for glioma, a detection rate of 98.26% for meningioma, a detection rate of 95.95% for pituitary, and a detection rate of 97.14% for normal images. [PERSON] et al. [35] developed a cutting-edge correlation learning method (CLM) for deep neural network structures that integrates the CNN with a conventional architecture. Meningioma (708 images), glioma (1426 images), and pituitary (930 images) tumors were among the 3064 brain cancers they investigated. Their designed CLM model had an accuracy of around 96%, a precision of about 95%, and a recall of about 95%. [PERSON] et al. [36] suggested the naive Bayes, random forest, neural network, KNN, and decision tree machine learning models for detecting brain tumors, as well as a hybrid ensemble classifier (KNN-RF-DT). They evaluated the machine learning models using 2556  
Figure 1: Outline of this paper. brain tumor images, with 85% of the data used for training and 15% for testing. For the classification, thirteen features were identified as a result of feature extraction by SWT, PCA, and GLCM. The proposed approach for identifying and categorizing brain tumors was evaluated, and the results showed that the method had an accuracy of 97.305%, a precision of 97.73%, a specificity of 97.60%, a sensitivity of 97.04%, and a reliability of 97.41%. [PERSON] et al. [37] proposed dense EfficientNet, which is a CNN-based network, to detect brain tumor images using MRI. The researchers also analyzed ResNet-50, MobileNet, and MobileNetV2, with their dense EfficientNet performing better. They obtained a 98.78% accuracy and a 98.0% F1-score after training the dense EfficientNet model. Four different types of MRI were employed in their research to identify brain tumors. The total dataset comprised 3260 MR images. [PERSON] et al. [38] introduced a CNN-based residual network for the early detection of brain tumors using a dataset of 2000 MR images. They employed the BRATS 2015 MRI dataset, and the findings for the residual networks were promising. The accuracy of their proposed model was 97.05%. Additionally, they considered other metrics, achieving a mean accuracy of 97.05%, a global accuracy of 94.43%, a mean IoU of 54.21%, a weighted IoU of 93.64%, and a mean BF score of 57.027%. One-hundred epochs were utilized during training to improve the performance. [PERSON] et al. [39] proposed a modified two-step dragonfly algorithm for brain tumor segmentation using 3D MR images. The greatest difficulties in identifying and segmenting the early stages of brain tumors are variations in the tumor size and structure. To overcome these challenges, the researchers employed a two-step dragonfly algorithm to precisely extract the original contour point. To obtain the results using the proposed model, they used BRATS 2017 3D MR brain tumor dataset.