# A Novel Data Augmentation-Based Brain Tumor Detection Using Convolutional Neural Network
## 2 Related Works  
In [21, 22], the authors provided an overview of some potential clinical use cases using deep learning techniques by defining the steps to undertake a deep learning project in radiology. The main idea of these two papers is to discuss opportunities and challenges for incorporating deep learning in the radiology practice of the future. The effectiveness of existing applications in radiology are not yet encouraging to say that the DL can replace a radiologist in all of his diagnostic work. However, radiologists and DL can help each other to give better results. Hence, several works have been done on the classification and segmentation of the brain using MRI images. [PERSON] et al. proposed a new method using SVD as a classifier to classify brain tumors. At the first level, the algorithm had been trained with normal brain MR images. Then, at the second level, it became capable of classifying the brain images into healthy and non-healthy images. The accuracy of this method reached up to 97%. In [23], [PERSON] et al. focused on brain tumor classification in MRI images using a classifier based on Convolutional Neural Networks (CNN). The main idea of the proposed approach is based on two steps. The first one is the tumor region segmentation using an ICA mixture mode model (Independent Component Analysis). The second step is the extraction of deep features. In [24], [PERSON] et al. proposed a novel convolutional neural network (CNN) based multi-grade brain tumor classification system. The first step consists of segmenting the tumor regions from an MR image using a deep learning technique. After that, they employed extensive data augmentation to train the system effectively. Finally, a pre-trained VGG-19 CNN model is fine-tuned using augmented data for brain tumor grade classification. [PERSON] et al. [25] trained a CNN model with an image processing technique to identify various brain tumor types and achieved 94.39% accuracy with an average precision of 93.33%. In [26], [PERSON] et al. used deep transfer learning to classify normal and abnormal brain MR images automatically. The proposed model that used ResNet34 has achieved a 5-fold classification accuracy of 100% on 613 MR images. [PERSON] et al., in [27], used the ResNet50 pre-trained model, and they removed the last 5 layers of the model, then they added 8 new layers. Then, comparing its accuracy with other pre-trained models such as GoogleNet, AlexNet, and ResNet50. The modified ResNet50 model showed effective results by achieving 97.2% accuracy. He obtained a 90% accuracy in the classified images as normal and abnormal in his proposed machine learning method. The authors in [28], proposed a modified AlexNet for the detection and classification of brain tumor images and obtained 91.6% of average classification accuracy. Another approach based on a modified ResNet50 model for brain tumor detection was developed in [29]. The proposed architecture is based on the ResNet50 model with a modified layer model including five convolutional layers and three fully connected layers. In [30], researchers proposed a brain tumor detection and classification. The main idea of their approach is to use a biologically inspired orthogonal wavelet transform and deep learning techniques. Techniques of graph theory were used [31] to detect abnormalities in brains. A VGG16 architecture was the main model to classify brain images in [32]. In this research paper, the authors described their approach based on the Mask R-CNN model to detect and identify brain tumors with improved precision. Limited datasets are a particularly common challenge in medical image analysis. Most computer vision tasks could use more data and data augmentation is one of the techniques often used to enhance the performance of computer vision systems. To overcome this limitation, many approaches based on deep learning have been proposed and detailed in the literature. One of the first applications of data augmentation was proposed in LeNet-5 [33] to classify the handwritten digit. In 2012, [PERSON] et al.