## 4 Results and Discussion
### Comparison with State-of-the-Art Related Work  
We compared the classification performance of the best deep neural network, i.e., Inceptionresnetv2, with existing methods for classifying brain tumors into meningioma, pituitary and glioma tumors. More specifically, we compared the proposed work with state-of-the-art DL approaches [4; 11; 16; 17; 24; 26; 28; 45]. [PERSON] et al. and [PERSON] et al. used the same TL techniques for brain tumor classification [17; 26]. [PERSON] et al. suggested a three-class classification approach based on deep TL [26]. A pre-trained GoogLeNet model was used for the feature extraction of brain MRI scans to distinguish between glioma, meningioma and pituitary cancers. Proven classification models were employed to classify the collected features. The experiment used a five-fold cross-validation strategy on an MRI dataset from figshare. With an average accuracy of 98 %, the suggested method exceeds all current state-of-the-art approaches. The performance metrics used were the area under the curve (AUC), precision, recall, F-score and specificity. According to the study results, TL appears to be a valuable method when the availability of medical images is limited.  
Researchers have used TL techniques and have succeeded in achieving the best results. Using a pre-trained VGG19 deep CNN model, [PERSON] et al. developed a block-wise fine-tuning technique based on TL [45]. A benchmark dataset of T1-weighted contrast-enhanced magnetic resonance imaging (CE-MRI) was used to test the proposed method. When validated in a five-fold cross-validation setting, the method achieved an average accuracy of 94.82% for the classification of meningioma, pituitary and glioma brain tumors. The proposed technique outperformed state-of-the-art classification on the CE-MRI dataset, according to experimental findings. [PERSON] et al. [17] proposed a framework and performed three studies to classify brain malignancies such as meningioma, glioma, and pituitary tumors utilizing three convolutional neural networks architectures (AlexNet, GoogLeNet, and VGGNet). Each study then investigated TL approaches, such as fine tuning and freezing, utilizing MRI slices from a brain tumor dataset (Figshare). Data augmentation techniques were used on MRI slices to help generalize results, increase dataset samples and reduce the risk of overfitting. In the presented studies, the fine-tuned VGG16 architecture achieved the greatest classification and detection accuracy of 98.69%.  
Table 5 shows a comprehensive comparison of different approaches based on accuracy. Only accuracy is included in Table 5 as a performance parameter because it is the most prevalent metric used in all relevant studies. According to our current knowledge, Inceptionresnetv2's TL beats all current state-of-the-art approaches in the literature. The proposed approach attains the best results because of its capability to extract more robust and distinctive deep features for classification. Moreover, we used a balanced dataset (_brain tumor classification (MRI) dataset_). In contrast, the datasets (CE-MRI) used in previous approaches such as [17; 26] and other approaches (mentioned in Table 5) were unbalanced, comprising 1426, 708, and 930 MRI images of glioma, meningioma, and pituitary brain tumors, respectively. The third column of Table 5 defines the percentage of the whole dataset used for training. We used 80% of the data for training all nine deep neural networks.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Model** & **Accuracy** \\ \hline Squeezenet & 97.28 \\ Alexnet & 97.83 \\ Inceptionresnetv2 & 98.01 \\ Inceptionv3 & 97.86 \\ Resnet101 & 98.01 \\ Resnet18 & 96.38 \\ Vgg19 & 97.46 \\ Shufflenet & 96.56 \\ Googlenet & 96.56 \\ Densenet201 & 98.37 \\ Resnet50 & 98.36 \\ Mobilenetv2 & 98.5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Accuracy comparison among the Inceptionresnetv2 and deep-features SVM approaches.