# A Novel Deep Learning Method for Recognition and Classification of Brain Tumors from MRI Images
## 4 Performance Evaluation
### Experimental Results and Discussion
#### 4.4.3 Comparison with Other Classification Techniques  
In this section, we present the comparison of classification results of our approach with results obtained by previous works over the same dataset [38]. Table 4 shows the comparison of tumor classification results with existing approaches in terms of average accuracy. In Table 4, we have presented the highest results of our proposed framework obtained using the DenseNet-41 backbone. In [66], the authors employed a pre-trained GoogleNet model by using transfer learning for feature extraction. The obtained features are classified by using three different classifiers: Softmax, SVM, and KNN, and achieved an accuracy of 97.1%. [PERSON] et al. [67] utilized various DL models: AlexNet, VGG16, and  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Technique**} & \multicolumn{2}{c}{**Segmentation Method**} & \multicolumn{2}{c}{**Evaluation Metrics**} \\ \cline{2-5}  & & **Mean IoU** & **Dice** & **Accuracy** \\ \hline [PERSON] et al. [62] & Cascaded CNN & 0.907 & 0.800 & - \\ [PERSON] et al. [63] & Faster RCNN and ChanVese active contour & - & 0.920 & 94.6 \\ [PERSON] et al. [61] & Active Contour and Fuzzy-C-Means & - & 0.665 & 91.0 \\ [PERSON] et al. [64] & Multi-scale CNN & - & 0.828 & - \\ [PERSON] et al. [65] & Traditional Mask-RCNN & 0.950 & 0.950 & 95.1 \\ Proposed method & Mask-RCNN (ResNet-50) & 0.951 & 0.955 & 95.9 \\ Mask-RCNN(DenseNet-41) & 0.957 & 0.959 & 96.3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the presented method with other segmentation techniques.  
VGG19. They fine-tuned VGG16 and VGG19 in a block-wise manner and AlexNet using the traditional layer-wise approach. The results showed VGG19 achieved better performance, with an average accuracy of 94.82 for brain tumor classification. [PERSON] et al. [68] proposed a deep CNN model with a modified activation function for brain tumor classification. The CNN model is constructed automatically by a network generator based on three different graph generation algorithms. The activation function is composed of Gaussian error linear units (GeLUs) and rectified linear units (ReLUs). The method achieved an accuracy of 95.49. However, the proposed approach is computationally complex due to network size. In [69], the authors proposed a hybrid feature extraction approach by using a PCA-based normalized GIST descriptor with a regularized extreme learning machine classifier. They obtained an overall accuracy of 94.93%. However, these approaches [66; 69] require preprocessed input images, i.e., the normalized pixel values from 0 to 1. In [70], a DL model named BrainMRNet employed three different processing methods such as attention modules, the hypercolumn technique, and residual blocks for brain tumor classification. Besides, the segmentation of the brain using the Otsu method is also performed to determine the lobe region (i.e., left, or right) with more concentrated tumorous cells. They obtained an accuracy of 97.69% for classification.  
From Table 4, it can be seen that in comparison to existing techniques, the proposed approach shows an improved overall accuracy of 98.34% for brain tumor type classification. The above-mentioned approaches [66; 67; 68] extract features from the whole image which may result in the misclassification of tumor type due to the complex nature of the tumor, i.e., overlapping boundaries and MRI artifacts. In [69], hand-crafted features are employed that are less discriminative and robust. The technique in [70] achieves results comparable to our approach; however, due to the hypercolumn technique, the model leads to overfitting and is computationally expensive, whereas the proposed approach employs deep features that are more discriminative and reliable. Moreover, region-based CNN first localizes the tumor region (RoI) and performs classification that results in improved accuracy.