# A Novel Deep Learning Method for Recognition and Classification of Brain Tumors from MRI Images
## 4 Performance Evaluation
### Experimental Results and Discussion
#### 4.4.2 Comparison with Other Segmentation Techniques  
In this section, we compare our proposed model with the other segmentation techniques using the Figshare brain tumor dataset [38] which is one of the largest online available datasets. For performance evaluation, we compared the average highest results of our presented technique with the average results reported in these studies [61; 62; 63; 64; 65]. For the presented technique, we have shown the results for the DenseNet-41-based Mask-RCNN framework as we obtained better performance on it as compared to the ResNet-50 framework. Table 3 shows a quantitative comparison using different performance measurement metrics such as mean IoU, dice score, and accuracy. [PERSON] et al. [61] proposed a pixel \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{5}{c}{**Evaluation Metrics**} \\ \cline{2-6}  & **Accuracy** & **mAP** & **Dice** & **Sensitivity** & **Time(s)** \\ \hline RCNN [51] & 0.920 & 0.910 & 0.870 & 0.950 & 0.47 \\ Faster RCNN [56] & 0.940 & 0.940 & 0.910 & 0.940 & 0.25 \\ Proposed (Resnet-50) & 0.959 & 0.946 & 0.955 & 0.953 & 0.20 \\ Proposed (Densenet-41) & 0.963 & 0.949 & 0.959 & 0.953 & 0.20 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison of our technique with other RCNN approaches. Figure 11: Confusion matrix of the presented technique using DenseNet-41. (**a**) Figshare Brain Tumor Dataset, (**b**) Brain MRI Dataset. based radius contraction and expansion (RCE) technique that uses an active contour model and fuzzy c-means for tumor segmentation. However, the performance depends on the threshold value set for extracting the region of interest. They obtained an average accuracy of 0.91. In [62], the authors proposed a cascaded dual scale LinkNet (CDSL Net), an end-to-end encoder-decoder-based architecture that uses multi-scale inputs for feature concatenation with corresponding layers in the network to perform brain tumor segmentation. They obtained a dice score of 0.8003. [PERSON] et al. [63] employed a faster RCNN model for tumor classification and then a Chan-Vese active contour algorithm for segmentation. They achieved an average dice score of 0.92 and an accuracy of 92.31 for tumor segmentation and classification, respectively. However, the authors considered only the axial MR images of glioma and meningioma brain tumors. In [64], the authors proposed a multi-scale CNN model that processes the input MR image in three different spatial scales using multiple processing pathways. They achieved an average accuracy of 0.973 and a dice score of 0.828 for classification and segmentation, respectively. In [65], we proposed a Mask-RCNN model with ResNet101 as a backbone for brain tumor detection and segmentation only. Our method in [65] achieved a dice score of 0.950; however, this method is computationally expensive due to the large number of parameters. As can be seen in Table 3, the proposed work does better than the other state-of-the-art techniques by achieving a dice sore of 0.959, a mean IoU of 0.957, and an overall average accuracy of 96.3% using DenseNet-41 for three different kinds of tumors. The proposed technique uses deep features that are more discriminating, reliable, and provide a more effective representation of tumor regions over other methods such as [61], which employs the hand-crafted features and is unable to better represent the tumor region due to structural complexities. Moreover, in some existing methods [62; 64] segmentation is applied directly to the entire image, which results in misclassification due to the complex background (i.e., brain tissues overlapping with tumor boundary, MRI artifacts, etc. ), which thus reduces the accuracy of the segmentation. The method in [63] employs a region-based method for tumor localization and requires further processing for tumor segmentation.