## 5 Conclusions  
This paper presents a comparative analysis of nine DL models for the classification of brain tumors through TL. The aim of this effort was to automate the process of detecting brain tumors by finding the best DL classifier for brain tumor classification. We applied TL to nine deep neural networks, i.e., Inceptionresnetv2, Inceptionv3, Xception, Resnet18, Resnet50, Resnet101, Shufflenet, Densenet201, and Mobilenetv2, and classified brain tumors into glioma, meningioma, and pituitary using a brain tumor classification (MRI) dataset. Our experimental findings validate that the Inceptionresnetv2 model achieved the most effective results for the classification of brain tumors. An accuracy of 98.71% signifies the effectiveness of Inceptionresnetv2 for reliable brain tumor classification. An accuracy of 98.91% for brain tumor classification has confirmed the superiority of the best model (Inceptionresnetv2) over other hybrid approaches in which we used DL models for deep features extraction and SVM for the classification of brain tumors. Although this paper explored TL of nine DL models for the classification of brain tumor MRI images, other models remain to be explored. Despite the limited data in our dataset, we have achieved satisfactory results. We applied data augmentation techniques to increase the size of the training dataset. However, the results can be further improved in the future by training the model with a larger dataset.  
Moreover, despite the accomplishments of this study, some improvements are still possible: firstly, the comparatively weak performance of the pre-trained DL models as stand-alone classifiers; secondly, significant training time elapsed by the transfer of learned deep neural networks; and thirdly, because of limited training data, the phenomenon of overfitting was observed. Future exploration in this domain can address these issues, possibly utilizing larger datasets for training and further tuning the transfer of learned deep neural networks. In the future, we will explore the TL of the remaining powerful deep neural networks for brain tumor detection and classification with less time complexity. Furthermore, we will also apply image segmentation techniques to improve the performance of our best performing model.  
[PERSON] developed the method; [PERSON], M.S.K., W.K. and J.A.K. performed the experiments and analysis; [PERSON], [PERSON], [PERSON] and A.S.S. wrote the paper. All authors have read and agreed to the published version of the manuscript.  
The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under grant number (RGP2/46/43). [PERSON] hint Abdulrahman University Researchers Supporting Project number (PNURSP2022R203), [PERSON] hint Abdulrahman University, Riyadh, Saudi Arabia.  
The datasets used in this investigation are available on request from the corresponding author.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Work** & **Method** & **Training Data** & **Accuracy** \\ \hline [PERSON] et al. [26] & CNN features SVM classification & 80\% & 95.82 \\ [PERSON] et al. [28] & Capsnet & 80\% & 86.56 \\ EI kader [31] & CNN–DWA & 80\% & 98.00 \\ [PERSON] [4] & BoW–SVM & 80\% & 91.28 \\ [PERSON] [24] & DWT–[PERSON] & 70\% & 91.90 \\ [PERSON] [16] & CNN–ELM & 70\% & 93.68 \\ [PERSON] [11] & Proposed CNN & 70\% & 84.19 \\ [PERSON] [45] & Block-wise fine tuning & 25–50–75\% & 94.82 \\ [PERSON] [17] & Transfer Learning & 70\% & 98.69 \\ Proposed approach & Inceptionresnetv2 & 80\% & 98.91 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison with existing approaches.