# Prediction of Glioma Grades Using Deep Learning with Wavelet Radionic Features
## 5 Discussion
This process was carried out with the Mann-Whitney U nonparametric test. With the model we applied, the number of radiomic features was reduced according to the semantic relationship, in this way, high accuracy was obtained in classification. However, in the studies conducted with the PCA method, the algorithm restricts the radiomic features and collects them in a certain category. However, it is difficult to classify Grade II and Grade III gliomas with high accuracy since their radiomic features are similar. Most of the methods developed in the literature are classified without using sufficient numbers of radiomic features. In a recent work by [PERSON] et al., they selected and classified only 3 features out of a total of 321 radiomic features. They reached accuracy values of between 0.70 and 0.76 with a total of 5 classifiers [80]. [PERSON] et al., studying 576 brain metastases of 161 patients, obtained 107 radiomic features and they determined 8 radiomic features with SPSS [81]. In another study, radiomic feature selection was applied and the radiomic features of Alzheimer patients were classified from brain images with accuracy of 91.5%, 83.1%, and 85.9%. As seen in these studies, the number of radiomic features used affects the accuracy rate. A total of 144 first-order features and 600 texture features, or a total of 744 3D wavelet features, were extracted with 3D Slicer, which is a very high number considering the literature. [PERSON] et al. [15] chose five radiomic features in glioma grading and three classifiers showed an average AUC value of 0.9400 for training groups and 0.9030 (logistic regression: 0.9010, support vector machine: 0.8866, and random forest: 0.9213) for test groups. In another study, 408 brain metastases were examined from 87 patients, 440 radiomic features were removed, and the AUC value was calculated with the random forest classifier [82]. In our study, a very high AUC result of 98.75% was achieved in the HHH wavelet group. Successful performance in radiomics depends on the type of data used. Support vector machines and logistic regression are useful in dividing cohorts into two different groups, such as good and bad prognosis, but they cannot provide detailed information when there are more variables. Another method commonly used in medical imaging in recent years is deep learning. Deep learning algorithms and radiomics are used together in medical image analysis, such as in image acquisition, segmentation, and classification in recent research. Increasing computer capacities and increasing medical imaging data allow this. [PERSON] et al. [83] classified the grades of glioma tumors using convolutional neural networks (CNNs). Their results showed reasonable performance in characterizing medical brain images with an accuracy of 91.16%. It is not always possible to obtain high accuracy in studies if too many features are obtained. In a deep learning-based study for brain tumor segmentation and survival prediction in glioma cases using the CNN architecture, 4524 radiomic features were extracted and 61% accuracy was achieved with the selected strong features [84]. We have classified brain tumors with DNN with high accuracy with the radiomic features that we obtained. The correct determination of radiomic features and the harmony of the parameters of the model have contributed significantly to these results. [PERSON] et al. [85] used the VGG-19 CNN architecture to classify brain tumor grades (I, II, III, and IV) and achieved 0.90 accuracy and 0.96 average precision. CNN is disadvantageous for large images such as \(256\times 256\), in cases where it is necessary to process a large number of filters [86]. In our study, eight sub-band wavelet filters were used and radiomic properties were extracted from each one separately. The DNN can obtain the features automatically by learning and has been applied in computer vision successfully [87]. For this reason, DNN was used in model selection. [PERSON] et al.