# Brain Tumor Detection with Deep Learning Methods' Classifier Optimization Using Medical Images
## 4 Experimental Results
### Machine Learning Method and Results  
Here, firstly, results regarding brain MR image classification were obtained using our dataset. Our purpose was to compare the results using pre-trained CNNs as feature extractors and different ML algorithms for classification. Feature extraction was performed using networks trained on ImageNet of the four architectures used in neural network prediction. This allowed us to leverage the knowledge captured by models in the form of learned features. The effectiveness of each pre-trained model was evaluated by measuring key performance metrics such as F1 score, recall, precision and accuracy. The machine learning algorithms LDA, SVM, K-NN, DT and NB use a model learned from the split dataset to make predictions or perform classifications for new data points. The models were trained using the Scikit-Learn library and default parameters were used. In addition, a vector of 30,056 features was created by combining the features obtained separately from four different CNN models (VGG, DenseNet, ResNet, SqueezeNet). This is the total number of features extracted separately from MR images for each model used. Here, the newly created features were trained with the same classification algorithms. Table 3 show the accuracy rates of the models and classification algorithms used in feature extraction.  
Looking at the model and classification accuracy rates, it was observed that the SVM classifier gave the highest accuracy rates for each model. Here, the features provided by the DenseNet algorithm showed the highest success rate when classified with SVM. In contrast, all classifications with DT showed very low accuracy rates. This may be due to the fact that the model does not fit the training data very well and cannot cope with the data. As a result, overfitting can be occur.  
Furthermore, ML classifiers were applied to the hybrid method that we developed. This method, the results of which have been observed through numerous experiments, proposes to classify trained CNN algorithms by combining them into binary, ternary and quadruple combinations. Figure 9 shows the DenseNet + SVM confusion matrix and Figure 10 shows the SqueezeNet + DenseNet + ResNet + VGG confusion matrix.  
Figures 11-14 show performance metrics for all classification methods. The figures show that all classification accuracy percentages vary. There may be many reasons for this. For example, the DenseNet + DT classification shows a low accuracy rate in general, but the SVM classification of the same model shows the highest accuracy rate. Therefore, only the confusion matrix for this classification is shown. In addition, the accuracy rate of the hybrid model, whose confusion matrix is shown in Figure 10, was found to be 83%. As a result of our initial training and classification studies, we can say that the results we found are quite successful considering similar studies in the literature. Based on these promising results, we further investigated the effectiveness of combining multiple features extracted by different pre-trained CNN models. In the second phase of our study, we optimized our trained data with the most appropriate parameter values to obtain more successful results.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**CNN Architecture** & **LDA** & **SVM** & **KNN** & **DT** & **NB** \\ \hline SqueezeNet & 0.780 & 0.848 & 0.815 & 0.762 & 0.785 \\ \hline DenseNet & 0.837 & 0.857 & 0.778 & 0.454 & 0.640 \\ \hline ResNet & 0.679 & 0.840 & 0.843 & 0.719 & 0.823 \\ \hline VGG & 0.661 & 0.808 & 0.795 & 0.694 & 0.765 \\ \hline SqueezeNet + DenseNet + ResNet + VGG & 0.837 & 0.833 & 0.736 & 0.487 & 0.634 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Machine learning accuracy rates.