## 3 Results and Discussion
In order to compare our results with those of previous studies, we selected only those papers which had designed neural networks, used whole images as input for classification, and tested their networks with a \(k\)-fold cross-validation method, as shown in Table 3. We also compared our results with those of researchers who had not tested the network with \(k\)-fold cross-validation, as shown in Table 4. A comparison with the studies that used designed neural networks and an augmented dataset, but did not test it by \(k\)-fold cross-validation, is presented in Table 5. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & \begin{tabular}{c} **\(k\)-Fold Cross-Validation** \\ **Method/Data Division** \\ \end{tabular} & \begin{tabular}{c} **Accuracy** \\ **[\%]** \\ \end{tabular} & \begin{tabular}{c} **Average** \\ **Precision [\%]** \\ \end{tabular} & \begin{tabular}{c} **Average** \\ **Recall [\%]** \\ \end{tabular} & \begin{tabular}{c} **Average** \\ **F1-Score [\%]** \\ \end{tabular} \\ \hline \hline \begin{tabular}{c} [PERSON] et al. \\ [28] \\ [PERSON] et al. \\ [29] \\ [PERSON] et al. \\ [30] \\ [PERSON] et al. \\ [31] \\ Proposed \\ \end{tabular} & \begin{tabular}{c} \(\delta\)-fold; data division not stated \\ 5-fold; 80% data in training set, \\ 20\% in test \\ \end{tabular} & 95.03 & X & X & X \\ \hline \hline \end{tabular} \begin{tabular}{c} [PERSON] et al. \\ [28] \\ [PERSON] et al. \\ [29] \\ [PERSON] et al. \\ [30] \\ [PERSON] et al. \\ [31] \\ Proposed \\ \end{tabular} & \begin{tabular}{c} \(\delta\)-fold; data division not stated \\ 5-fold; 80% data in training set, \\ 20\% in validation, 20\% in test. \\ \end{tabular} & 93.68 & X & X & X \\ \hline \hline \end{tabular} \begin{tabular}{c} \(\delta\)-fold; data division not stated \\ 5-fold; 80% data in training set, \\ 20\% in test \\ \end{tabular} & 92.61 & X & X & X \\ \hline \hline \end{tabular} \
\begin{tabular}{c} \(\delta\)-fold; data division not stated \\ 5-fold; 80% data in training set, \\ 20\% in test \\ \end{tabular} & 93.68 & 94.60 & 91.43 & 93.00 \\ \hline \hline \end{tabular}
\begin{tabular}{c} \(\delta\)-fold; data in training set, \\ 20\% in validation, 20\% in test. \\ \end{tabular} & 95.40 1 & 94.81 1 & 95.07 1 & 94.94 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of results of different network architectures, trained and tested on the original dataset, which use whole images as input and are tested using the \(k\)-fold cross-validation method. Figure 7: Confusion matrices for the augmented dataset with subject-wise 10-fold cross-validation during testing. In the literature, there are also studies that used the same database for classification with pre-trained networks [23; 35; 36; 37; 38; 39; 40] or, as input, they use only tumor region or some features that are extracted from the tumor region [7; 21; 23; 41; 42]. Similarly, in several papers, researchers have modified this database prior to classification [36; 43; 44; 45; 46; 47]. The designed networks are usually simpler than already-existing pre-trained networks and have faster execution speed. To our knowledge, the best results using the pre-trained network are 98.69% [40] and 98.66% accuracy [36]. [PERSON] et al. [40] preprocessed images with contrast enhancement and augmented the dataset. The augmentation was fivefold, with rotations of 90, 180, and 270 degrees and horizontal and vertical flipping. The best result was obtained with a fine-tuned VGG16 trained using stochastic gradient descent with momentum. Although our approach has a 1.41% higher classification error, it has 4.3 million weights as opposed to the VGG16, which is a very deep network with 138 million weights. Very deep networks such as VGG16 and AlexNet require dedicated hardware for real-time performance. [PERSON] and [PERSON] [36] also modified the database, using only those images that were taken in the axial plane, and used only 100 images of each tumor type.