# A Deep Analysis of Brain Tumor Detection from MR Images Using Deep Learning Networks
## 5 Results' Analysis and Discussion  
The results of various types of developed deep learning models--i.e., the VGG16, CNN, ResNet-50, and Inception V3 classification algorithms--on the brain tumor MR image dataset are analyzed in Table 3, and comparisons are shown in Figure 8. In Table 3, we present the performance of the models with respect to the accuracy, area under the curve (AUC), recall, and loss function results. After analyzing the methods of the CNN, VGG16, ResNet-50, and Inception V3, it was observed that the CNN outperformed the other deep learning models based on the findings in Table 1. The CNN achieved a validation accuracy of 93.3%, a validation AUC of 98.43%, a validation recall of 91.1%, and a validation loss of 0.260. In Figure 9, the validation accuracy with respect to the training accuracy graphs for the CNN, ResNet-50, Inception V3, and VGG16 are presented accordingly. The blue lines present the training accuracy, and the orange lines present the validation accuracy. Here, the CNN achieved the highest validation accuracy of 93.30% with a training accuracy value of 90.50%. ResNet-50 achieved a validation accuracy of 81.10% with the highest training accuracy value of 98.43%. Inception V3 achieved a validation accuracy of 80% and a training accuracy of 91.79%. However, VGG16 achieved the least validation accuracy of 71.60% and the least training accuracy of 79.20%. While implementing the models, the epochs were selected to be 80, and the batch size was selected to be 18 with the Adam optimizer. According to the accuracy graph analysis, the CNN performed better than the other models, because the validation accuracy had a great output curve with respect to the training accuracy, and no over-fitting or under-fitting problems occurred. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models** & **Accuracy (\%)** & **AUC (\%)** & **Recall (\%)** & **Loss** \\ \hline CNN & 93.30 & 98.43 & 91.13 & 0.25 \\ ResNet-50 & 81.10 & 94.20 & 81.04 & 0.85 \\ VGG16 & 71.60 & 89.60 & 70.03 & 1.18 \\ Inception V3 & 80.00 & 89.14 & 79.81 & 3.67 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Deep learning models’ performance on brain tumor detection. Figure 8: Performance analysis of the proposed model in terms of the accuracy, AUC, and loss. In Figure 10, the validation AUC with respect to the training AUC graphs for the CNN, ResNet-50, VGG16, and Inception V3 are presented accordingly. The blue lines present the training AUC, and the orange lines present the validation AUC. Here, the CNN achieved the highest validation accuracy of 93.30% with a training accuracy value of 90.50%. The AUC determines the model's performance and assesses the model's ability to differentiate between classes. The higher the AUC value, the better the model's performance is. Here, the CNN achieved the highest validation AUC of 98.43% with a training AUC value of 98.40%. ResNet-50 achieved a validation AUC of 94.20% with the highest training AUC value of 99.95%. Inception V3 achieved the least validation AUC of 89.14% and a training accuracy of 96.97%. However, VGG16 achieved a validation AUC of 89.60% and the least training accuracy of 95.32%. In Figure 11, the validation loss with respect to the training loss graphs for the CNN, ResNet-50, VGG16, and Inception V3 are presented accordingly. The blue lines present the training loss, and the orange lines present the validation loss. The loss is a penalty for making an incorrect prediction. However, the loss is a number that indicates how inaccurate the model's prediction is at each epoch. If the loss is zero, the model's prediction is perfect; otherwise, the loss is greater. To calculate the loss in the detection process, we used the categorical cross-entropy loss function. The categorical cross-entropy is a loss function that is mostly used in multi-class classification tasks. Here, the CNN achieved the lowest validation loss of 0.250 with a training loss value of 0.289. ResNet-50 achieved a validation loss of 0.853 with a very low training loss value of 0.063.