## 4 Results and Discussion
### Results  
This section discusses the performance of different pre-trained TL classifiers used to classify brain MRI images from the brain tumor classification (MRI) dataset into meningioma, pituitary and glioma. The main advantage of TL classifiers and fine tuning is decreasing overfitting issues that frequently occur in DL algorithms when experimenting with a smaller sample of training and testing images. All the TL models were trained and validated on the same TL settings indicated in Table 2 for the classification of brain tumors. We used 2762 brain tumor classification (MRI) images to classify brain tumors. Table 3 represents the detailed results of various TL algorithms in classifying brain tumor images and shows that each TL classifier achieved satisfactory results. We analyzed and evaluated the TL algorithms using accuracy, precision, recall and f-measure evaluation metrics. The results show that the inceptionresnetv2 DL model achieved the best average accuracy of 98.91%, and resnet50 achieved the lowest average accuracy of 67.03%. In contrast, the TL of the remaining seven deep neural networks achieved average classification accuracy. It is essential to mention that variants of the Resnet framework achieved different results. Resnet18 achieved a minimum accuracy of 67.03%, and Resnet50 attained an accuracy of 67.03%, whereas Resnet101 achieved an accuracy of 74.09%, which is the highest among all variants. The training and validation process of our best performing deep neural network, i.e., Inceptionresnetv2, is shown in Figure 4. The elapsed time returns the total CPU time used by the DL model since it was started, which is the time taken by the model to process (classify) all images of the dataset. Since we used the data augmentation \begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Optimization algorithm & SCDM \\ Maximum Epochs & 14 \\ Learning rate & 0.01 \\ Verbose & False \\ Validation frequency & 30 \\ Shuffle & Every epoch \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parameters of all transfer learning architectures. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F-Measure** & **Elapsed Time** \\ \hline Inceptionresnetv2 & 98.91 & 98.28 & 99.75 & 99.00 & 1252 min 50 s \\ Inceptionv3 & 94.48 & 93.00 & 94.5 & 93.74 & 667 min 47 s \\ Xception & 98.37 & 98.51 & 99.25 & 98.87 & 1730 min 25 s \\ Resnet101 & 74.09 & 73.19 & 67.23 & 70.08 & 801 min 36 s \\ Resnet18 & 63.04 & 64.63 & 52.09 & 57.68 & 187 min 47 s \\ Shufflenet & 89.31 & 87.96 & 87.43 & 87.69 & 159 min 25 s \\ Densenet201 & 68.71 & 73.04 & 67.46 & 70.14 & 950 min 1 s \\ Resnet50 & 67.03 & 70.55 & 68.13 & 69.32 & 525 min 14 s \\ Mobilenetv2 & 82.61 & 81.11 & 80.32 & 80.71 & 318 min 15 s \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Average classification accuracy. technique, which significantly increased the dataset's size, all models took considerable time for classification. This time also depended on the depth and architectural design of the models. The elapsed time is expressed in seconds. The Shufflenet TL model was the most efficient elapsed time classifier, achieving satisfactory classification results and taking the shortest time, 159 min, for brain tumor classification. In contrast, the Xception TL model took a maximum time of 1730 min 25 sec to identify and classify brain tumor MRI images into different types. The Shufflenet model is fast because it uses two new operations, i.e., channel shuffle and pointwise group convolution, significantly reducing computation costs while retaining accuracy. It should be noted that the classification time for different variants of the Resnet TL classifiers increases with the number of framework layers. For example, Resnet18 took a minimum time of 187 min 47 sec, and Resnet50 took 525 min 14 sec. Additionally, Resnet101 took the maximum time of 801 min 36 sec to classify brain tumors into meningioma, pituitary and glioma.