# Prediction of Glioma Grades Using Deep Learning with Wavelet Radionic Features
## 5 Discussion
The experimental results obtained showed that the GTV and BTV segmentations were statistically related (Spearman rank correlation coefficient: 0.898) but did not have a higher degree of similarity (mean Membrane Similarity Coefficient: \(61.87\pm 14.64\)). On the other hand, only MRI images were used in our study for the segmentation process [74]. Accordingly, in our study, the most effective and significant parameters related to glioma grades in eight sub band and high band wavelet groups were run. As a result, different accuracy values were obtained in each group. In a study comparing the GrowCut algorithm with GraphCut, GrabCut, and Random Walker algorithms, it was reported that the GrowCut algorithm segmented images faster and with higher quality [25]. Some of the basic features that exist in the Growcut algorithm have been an important factor in choosing it for segmentation. (1) Natural handling of images, (2) Multi-label segmentation, (3) easy to use and apply, (4) providing user input opportunity [75]. In this study, 3D Slicer, a free open source software platform for biomedical research, and the GrowCut algorithm, integrated with 3D Slicer, were used to segment brain tumors. This increased the originality and validity of the present study. Different parameters show statistical significance in each group and that is an indicator that a single parameter is not effective in glioma grading. It is now known that texture features and especially quantitative features are important variables in the field of radiomics. [PERSON] et al. [76] used 2D wavelet transform and a spatial gray-level dependency matrix in feature extraction. In this study, 3D wavelet transform is used and the classification results of radiomic features obtained from eight different wavelet groups are evaluated. The purpose of feature selection is to minimize the number of extracted features in the most meaningful way with the right methods. These features should firmly define the concepts hidden in the data. Over-selection of data can give excellent results when applied to the training data of the analysis, but when new data are presented to the algorithm for analysis, it is an important problem in machine learning and the performance of the model decreases. Our results showed that the First-order Energy, GLDM Dependence Non-Uniformity, GLSZM Gray Level Non-Uniformity, and NGTDM Coarsenessfeatures of 3D wavelet filters had high power in differentiating between Grade II and Grade III in each MRI sequence. [PERSON] et al. identified and used the most stable radiomic properties (First-order Energy, Shape-Compactness, Texture-Gray Level Non-Uniformity (GLNU), and Wavelet HLH-GLNU) in their study [77]. In addition, it is very difficult for the human eye to detect and quantify some statistical features such as wavelet and first-order features [37]. Although automated tools significantly affect the process of identifying the ROI and reduce the variable results among radiologists, they are not yet fully and efficiently used. For this reason, ROI fields created by experts in the dataset have been tested here with different automatic segmentation tools and high accuracy has been achieved. In the classification of low-grade and high-grade glioma, authors stated that correlation with GLCM has the highest discrimination among texture features [78]. In our study, correlation, Idn, and Idmn GLCM matrix features also revealed significant differences between Grade II and Grade III. Radiomic features obtained as a result of radiomic analysis have a very important place in classification. In another study, the authors [79] used DWT in their study on brain tumor, but they performed wavelet feature selection using PCA. This is a mathematical operation that converts a set of semantically related variables into fewer unrelated variables called principal components. In our study, the radiomic feature extraction process and the similarity between Grade II and Grade III groups in each feature are examined.