## 4 Conclusions  
A new CNN architecture for brain tumor classification was presented in this study. The classification was performed using a T1-weighted contrast-enhanced MRI image database which contains three tumor types. As input, we used whole images, so it was not necessary to perform any preprocessing or segmentation of the tumors. Our designed neural network is simpler than \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **Data Division** & **Accuracy** & **Average** & **Average** & **Average** \\  & & **[\%]** & **Precision [\%]** & **Recall [\%]** & **F1-Score [\%]** \\ \hline [PERSON] et al. [32] & data division not stated & 86.56 & X & X & X \\ [PERSON] et al. & 80\% data in training set, 20\% & 92.60 & 92.67 & 94.67 & 93.33 \\ [PERSON] et al. & 75\% data in training set, 25\% & 93.30 & X & 91.00 & 72.00 \\ \multirow{2}{*}{Proposed} & \multirow{2}{*}{60\% data in training set, 20\% in validation, 20\% in test} & \multirow{2}{*}{97.39 1} & \multirow{2}{*}{95.44 1} & \multirow{2}{*}{96.94 1} & \multirow{2}{*}{96.11} \\  & & & & & \\ \hline \hline \end{tabular} \({}^{1}\) The best obtained result.  
\end{table} 
Table 4: Comparison of results of different network architectures, trained, and tested on the original dataset, which use whole images as input and are not tested using the \(k\)-fold cross-validation method. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **Data Division** & **Accuracy** & **Average** & **Average** & **Average** \\  & & **[\%]** & **Precision [\%]** & **Recall [\%]** & **F1-Score [\%]** \\ \hline [PERSON] et al. [35] & 68\% data in training set, 32\% in validation and test & 96.13 & 96.06 & 94.43 & X \\ Proposed & 60\% data in training set, 20\% in validation, 20\% in test & 97.28 1 & 97.15 1 & 97.82 1 & 97.47 1 \\ \hline \hline \end{tabular} \({}^{1}\) The best obtained result.  
\end{table} 
Table 5: Comparison of results of different network architectures, trained and tested on the augmented dataset, which use whole images as input and are not tested using the \(k\)-fold cross-validation method. pre-trained networks, and it is possible to run it on conventional modern personal computers. This is possible because the algorithm requires many less resources for both training and implementation. The importance of developing smaller networks is also linked to the possibility of deploying the algorithm on mobile platforms, which is significant for diagnostics in developing countries [48]. In addition, the network has a very good execution speed of 15 ms per image. In order to test the network, we used record-wise and subject-wise _10_-fold cross-validation on both the original and augmented image database. In clinical diagnostics, the generalization capability implies predictions for subjects from whom we have no observations. With this in mind, the observations from individuals in the training set must not appear in the test set. If this condition is not met, complex predictors can have unrealistically high prediction accuracy due to the confounding dependency between the identity and the diagnosis of a patient [26]. In relation to that knowledge, we have committed subject-wise cross-validation. A comparison with the comparable state-of-the-art methods shows that our network obtained better results. The best result for _10_-fold cross-validation was achieved for the record-wise method and, for the augmented dataset, and the accuracy was 96.56%. To our knowledge, in the literature, there is no paper that shows tested generalization, through subject-wise _k_-fold method, for this image database. For the subject-wise approach, we obtained an accuracy of 88.48% for the augmented dataset. The average test execution was less than 15 ms per image. These results show that our network has a good generalization capability and good execution speed, so it could be used as an effective decision-support tool for radiologists in medical diagnostics.