# Prediction of Glioma Grades Using Deep Learning with Wavelet Radionic Features
## 3 Materials and Methods
### Deep Neural Network (DNN) Model  
Deep learning extends traditional neural networks by adding hidden layers to network architectures between input and output layers while modeling more complex and nonlinear situations [53]. This situation has attracted the attention of researchers with its performance in many areas such as image processing, classification, and medical image analysis. Deep learning has been used frequently in recent years in medical imaging and computer-aided radiological research. The increasing availability of medical image data as well as the increasing data processing speeds of computers have had a major impact on this [54].  
There are various DL architectures, among which Convolutional Neural Networks (CNNs) have been widely used in recent years. CNN architecture does not require feature extraction before implementation. In the other side training CNN from scratch is time consuming and difficult. Before the model is ready, it needs a very large data set for compilation and training [55]. In this case, it is not correct to apply CNN in every dataset. Our proposed methodology is based on the DNN learning architecture for classifying brain tumors as Grade II and Grade III. In this way, radiomic features were obtained in different categories. In such a way, high success is achieved in many problems, including image processing [56], video processing [57], and speech recognition [58]. As with the model applied in this study, using radiomic features in the same architecture with deep learning algorithms can improve outcome estimation.  
High classification results have been obtained in deep learning studies with fewer than 100 patients recently [59; 60; 61]. Considering all these criteria, this study was designed with the open source H2O Python module, which is a feedforward DNN trained using backpropagation [62]. The most important parameters in the H2O deep learning module are the number of periods, the number of hidden layers and the number of neurons in the hidden layer. For nonlinear problems, it has been suggested to start with two hidden layers. The larger the size of the hidden layer, the easier it will be to learn. But after a certain point the success rate will probably decrease [63]. For example, [PERSON] and [PERSON] showed in their study that the network structure of \(400\times 400\times 400\) reached lower error rates than similar network structures [64].  
Different parameters and layers were used to increase network robustness. The deep learning architecture used comprises \(200\times 200\) hidden layers. While the activation function used in the hidden layers is Rectifier Linear Unit (ReLu), the activation function used in the output layer is Softmax.  
The DNN hyper parameters are shown in Table 1. Stochastic gradient descent (SGD) was used as the optimizer. The epsilon value was \(10^{-8}\). Moreover, elastic regularization was performed to prevent over-fitting. Five-fold cross-validation was performed within the framework of training, the Bernoulli distribution process was performed for the dataset, and the values were calculated.  
The performance of both groups was measured using ROC curves and the accuracy, sensitivity, specificity, and area under the ROC curve (AUC) values. Selected radiomic features of each transform band were classified. Accuracy levels of Grade II and Grade III tumors were examined comparatively according to wavelet groups. The Computational Complexity of the Classification Method is presented in the Table 2.