# Prediction of Glioma Grades Using Deep Learning with Wavelet Radionic Features
## 4 Results
### Performance Evaluation  
Classification was performed with the H2O controlled learning model, and 60% of the dataset was randomly reserved for training while 20% was reserved for validation and 20% for testing. Dataset splitting was done before the training phase. Table 3 shows the number of patients to be classified according to these groups.  
The classification was implemented in an open source Python environment. After the training phase with the DNN model, it was tested on a dataset that had never been used in training to provide an unbiased assessment. Figure 5 shows the confusion matrices resulting from classification in all wavelet sub-bands.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Groups** & **Grade II** & **Grade III** & **Total** \\ \hline Training & 49 & 25 & 74 \\ Validation & 12 & 9 & 21 \\ Testing & 16 & 10 & 26 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Training, validation, and testing statistics.  
Figure 5: _Cont_.  
Table 4 presents the confusion matrix structure. Equations (1)-(4) show the formulas for the evaluated performance metrics.  
Accuracy: The most commonly used classification accuracy to determine classification performances is used to measure the overall effectiveness of the classifier.  
\[\text{Accuracy}=\frac{\text{tp}+\text{tn}}{\text{tp}+\text{tn}+\text{fp}+\text{ fn}} \tag{1}\]  
Precision: The term precision is used in describing the agreement of a set of results among themselves. Precision is usually expressed in terms of the deviation of a set of results from the arithmetic mean of the set.  
\[\text{Precision}=\frac{\text{tp}}{\text{tp}+\text{fp}} \tag{2}\]  
Recall: Recall analysis determines how different values of an independent variable affect a particular dependent variable under a set of assumptions. This process is used within certain limits that depend on one or more input variables.  
\[\text{Recall}=\frac{\text{tp}}{\text{tp}+\text{fn}} \tag{3}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \multicolumn{2}{c}{**Predicted Class**} \\ \cline{3-4}  & & **Positive** & **Negative** \\ \hline \multirow{2}{*}{Actual Class} & Positive & tp (true positive) & fn (false negative) \\ \cline{2-4}  & Negative & fp (false positive) & tn (true negative) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Confusion Matrix.  
Figure 5: Confusion matrices of all wavelet sub-bands.  
F1 Score: In order to make a sound decision about classifier performance, results other than classification accuracy should also be evaluated. The F1 score calculated for this purpose measures the relationship between the positive information of the data and those given by the classifier.  
\[\text{F1 score}= \frac{2\text{tp}}{2\text{tp}+\text{fp}+\text{fn}} \tag{4}\]  
The classification success of the model was evaluated with precision, recall, and F1 metrics. These performance metrics were obtained through the confusion matrix. Table 5 shows the classification performance statistics.  
When the test results were examined, accuracy of 96.15% was obtained for w-HHH compared to 84.62% for w-HHL, 73.08% for w-HLH, 80.77% for w-HLL, 84.62% for w-LHH, 76.92% for w-LHL, 84.62% for w-LLH, and 80.77% for w-LLL. Thus, w-HHH has higher accuracy (96.15%) when compared with all other 3D wavelet transform bands. The area under the ROC curve (AUC) is also generally examined [65]. When AUC values are analyzed, w-HHH has the highest AUC value of 98.75%. Figure 6 shows the ROC curve for w-HHH filter classification.