# A Novel Data Augmentation-Based Brain Tumor Detection Using Convolutional Neural Network
## 6 Results and Discussions  
VGG-16 is a very good architecture for benchmarking a particular task. Also, pre-trained networks for VGG are available freely on the internet, so it is commonly used out of the box for various applications. Although VGG-16 represented the acquired features effectively, deep structure and supervised learning may cause overproduction when the quantity of training data is restricted. It is the case with many medical situations where there is limited data, and the VGG-16 model, which is characterized by many parameters, may lead to over-fitting. In the VGG-16 model, there are thirteen convolution layers, five pooling layers, and always uses a \(3\times 3\) Kernel for convolution and \(2\times 2\) size of the max-pooling layer. In our work, we create a CNN architecture using Tensorflow, Keras, and jupyter. We train the model on an MRI brain image database. A block diagram of our solution is described in Figure 7. The output of the model is \"YES\" for the abnormal brain (Figure 8) or \"NO\" for the normal brain (Figure 9).  
The running time is about 300 (s). The results demonstrate the model's ability and accuracy in classifying images (Figure 10). To confirm the obtained results, we evaluated our model on a second Kaggle dataset. Its accuracy is shown in Figure 11.  
Figure 8: Abnormal brains.  
Figure 10: Accuracy and loss for training and validation.  
Figure 9: Normal brains.  
As shown in Table 3, the used dataset of brain tumors consists of 253 real brain images (Kaggle platform). Data are divided into training (185 images), validation (48 images), and testing (20 images). Then, this dataset of 253 images was augmented to produce 3700 new images. The proposed model was trained using the augmented dataset and the results are described in Table 4.  
The number of epochs = 15 and the Batch size = 32. To evaluate the proposed model's efficiency, we relied on ten models of machine learning methods. The comparison between the proposed VGG-16 model and the other models was based on the following values [67,68]:  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{FP}+\text{TN}+ \text{FN}}\times 100 \tag{3}\]  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{4}\]  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{5}\]  
\[\text{F1-score}=2\times\frac{\text{Precision}\times\text{Recall}}{\text{ Precision}+\text{Recall}} \tag{6}\]  
For comparison purposes, true positive (TP), true negative (TN), false positive (FP), and false negative (FN) are used to assess the performance of the proposed model, on the one hand, and ten machine learning models, on the other. Table 4 illustrates the comparison between the different models.  
Figure 11: Accuracy and loss for training and validation (Dataset 2).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Dataset** & **Number of Images** \\ \hline Original dataset & 253 \\ Tumor brain MRI images & 155 \\ Non-tumor MRI images & 98 \\ After augmentation & 3700 \\ Training & 185 \\ Validation & 48 \\ Test & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Dataset before and after augmentation.  
The VGG16 model accomplished the best values of Accuracy, Precision, Recall, and F1-score.