# Prediction of Glioma Grades Using Deep Learning with Wavelet Radionic Features
## 1 Introduction  
Gliomas are primary malignant tumors that are common in the brain, having a high relapse rate and high mortality. According to the data of the American Cancer Society, 23,880 people were diagnosed with malignant brain and spinal cord tumors in 2018 and 70% of those diagnosed with malignant tumors died [1]. Gliomas are usually classified in a range of grades from I to IV. According to the classification of the World Health Organization (WHO), gliomas can be subdivided by their malignancy from Grade II (lower grade) to Grade IV (high grade) [2]. Gliomas, hypoyphosis tumors and meningiomas are among the primary brain tumors [3]. The WHO categorization includes low-grade gliomas (LGGs), diffuse low-grade (Grade II) and medium-grade gliomas (Grade III), and tumors with highly variable behaviors whose textural structures are unpredictable. In addition, according to the WHO, low-grade gliomas are infiltrative neoplasms that usually contain low and medium-grade gliomas (Grade II and Grade III) [4]. Grade II and Grade III brain tumors can have types of astrocytoma, oligoastrocytoma, oligodendroglioma. These tumors can be in Grade II and Grade III groups, therefore, examining brain tumor types in the right classes will facilitate the treatment of brain cancers. Astrocytomas can below grade (Grade II) and high grade (Grade III). Low grade astrocytomas grow slowly in a certain area. High-grade astrocytomas grow rapidly and require different treatment methods [5]. The tissues of Grade II tumors are malignant and these cells do not look very much like normal cells. Grade III tumors are malignant tissue cells and these cells also grow actively [5]. Classifying Grade II and Grade III tumors, both cellular and MRI images, is quite complex and clinically demanding. In the treatment of gliomas, the desired level of progress has not been achieved yet. Accurate grading of the tumor area in the treatment of gliomas is very important. Classical surgery is risky in cases of tumors located in deep and critical areas of the brain. In such cases, it is possible to identify the grade of the tumor and to plan the next treatment accordingly by performing a safer and high-tech stereotaxic biopsy, histopathological examination and then remove the tumor with a smaller surgery. Knowing whether the tumor is benign or malignant and determining the grade of the tumor allows the patient to be better informed about the treatment method to be applied. Agnostic radiomic features are quantitative descriptors that extract the radiomic features of medical images using multiple data characterization algorithms and investigate the relationship between images and underlying genetic features [6]. The multivariate radiomic features that are automatically revealed have promising effects in the identification of prognostic factors that provide information about the clinical course at the time of diagnosis, regardless of treatment [7]. Radiomics is a method of obtaining many quantitative parameters of radiological images to analyze the texture and complex structure of lesions [8]. Radiological imaging is an important step used for the localization of the tumor, preliminary diagnosis, surgical planning, determination of the amount of postoperative tumor resection, and planning the treatment and follow-up in patients with glioma. In recent years, radiomics has developed as a method that detects medical image properties obtained by imaging methods, such as shapes, first-order features, textures, or wavelet properties, without requiring surgical procedures [9]. Several studies have used various feature selection methods to estimate and classify glioma grades with accuracy [10; 11], texture [12], and MRI radiomic features [13; 14; 15]. As these prominent methods show, diagnostic accuracy will increase with the quantitative evaluation of the classical information (structural, functional, etc.) of MR images by advanced image analysis techniques such as morphological and texture analysis [16].