# A Novel Deep Learning Method for Recognition and Classification of Brain Tumors from MRI Images
## 4 Performance Evaluation
### Experimental Results and Discussion  
This section contains a detailed analysis and discussion of the obtained results. We have experimented on real MRI images using two datasets [38; 39]. The proposed model uses two different deep neural networks, namely ResNet-50 and DenseNet-41 as backbone networks to learn deep features automatically from the training images. However, we obtained better results with DenseNet-41-based Mask-RCNN due to its ability to compute more robust features as compared to ResNet-50. Some of the visual results for custom Mask-RCNN using both datasets are presented in Figure 8. From the figure, we can observe that the presented approach (with DenseNet-41) can more accurately localize the brain tumor from the healthy tissues despite discontinuous or blurry boundaries and artifacts in MRI samples. Moreover, the custom Mask-RCNN (with DenseNet-41) method can precisely segment the brain tumor by overcoming the challenges of location, shape, and size.  
As discussed earlier, the obtained results are evaluated using various quantitative measures such as accuracy, precision, recall, DSC, and IoU. To further understand the accuracy of our method, we have drawn a boxplot for evaluation matrices using both datasets, which is shown in Figure 9. The boxplot represents the spread of results into four quartiles, median, and an outlier for all input training images. Part (a) of the figure shows the results obtained over the Fyshare dataset while part (b) presents the results for the MRI dataset with the DenseNet-41 based Mask-RCNN framework. From Figure 9, it can be seen that our approach attains better results for the Fyshare database as compared to the MRI dataset. The presented approach has achieved an average accuracy and dice score of 95.9% and 0.955 with ResNet-50 and 96.3% and 0.959 using DenseNet-41 over Fyshare dataset. The mean average precision (mAP) of the proposed method to localize the brain tumor region at the regression layer is 0.949. In the case of ResNet-50-based Mask-RCNN, the presented technique fails to accurately localize the brain tumor in a few images due to the visual similarity with healthy tissues, as shown in Figure 10. However, for the DenseNet-41-based network, the system has exhibited more accurate results than ResNet-50.  
Figure 6: Pictorial representation of precision.  
Figure 7: Pictorial representation of recall.  
Figure 11 shows the confusion matrices that summarize the classification results of the proposed technique against the ground truth for the DenseNet-41-based network using Figshare [38] and Brain MRI [39] datasets. For both datasets, the presented technique attains better results with DenseNet-41-based Mask-RCNN, as reported in Figure 11. Here, part (a) presents the classification accuracy for the Figshare dataset while part (b) shows the classification results for the MRI dataset. The presented approach obtains an overall classification accuracy of 98.34% for the Figshare dataset while it achieves the accuracy value of 97.90% for the Brain MRI dataset. In the case of the Figshare dataset, the attained classification accuracies for glioma, meningioma, and pituitary tumor types over DenseNet-41 are 98.62%, 97.81%, and 98.60%, respectively, while for the Brain MRI dataset, the presented technique attains the class-wise accuracy of 97.74% and 98.06% for tumor and non-tumor classes, respectively.  
Figure 8: Example segmentation results of high-score-obtaining test images using the proposed method. The red contour shows the predicted tumor mask.  
Figure 9: Tumor localization results of the proposed approach over datasets using DenseNet-41 (**a**) Figshare, (**b**) Brain MRI dataset. + sign shows the outer value which is larger than the other values.