### Literature Review  
In surveying the image analysis published literature, a series of research studies describe the use of imaging methodologies in conjunction with with computer-aided diagnosis for the classification and segmentation of brain tumors. For example, in work by [PERSON] et al. the authors implemented the Convolutional Neural Network (ConvNet) as an artificial neural network for the purpose of segmenting the brain tumor based on pixel-level labeling [16]. They obtained a Dice similarity coefficient metric of 78%, 65% and 75% for the complete, core and enhancing brain tumor regions, respectively. This work is contrasted with the work proposed in the current study since their classification of brain tumor did not consider sub-grades of tumor classification using image-level labeling. Similarly, in [PERSON]'s study, the authors presented an automatic grading tool of glioma using deep learning [5]. However, their tool used digital pathology images, which are acquired from an invasive imaging technique. The tool proposed in the current report uses a non-invasive imaging technique, which is based on fluid-attenuated inversion recovery (FLAIR)-weighted MR images. In studies by [PERSON] et al. the authors used Linear Discriminant Analysis (LDA) to classify brain tumors using 1H short-echo spectra [17]. They obtained near 90% correct classification for two out of three datasets they tested with their algorithm. In additional studies by [PERSON] et al. the authors used single voxel proton MR spectroscopy at different values of Echo Time (TE) to classify the signal into four classes including meningioma, low-grade astrocytoma, anaplastic astrocytoma and glioblastoma-metastases [18]. They obtained an accurate classification rate of 81% on the dataset of short TE values. The classification methodology addressed in the study by [PERSON] et al. attempted to classify the samples into two classes: benign and malignant. In this study, the database utilized consisted of MR spectroscopy data [19]. After implementing several machine learning methods in this study, a sensitivity of 86.1% was achieved using the random forest method. The classification solutions in both these earlier works were based on the use of single voxel proton MR spectroscopy unlike the solution proposed in our work, where we used FLAIR-weighted MR images [18; 19]. Additional studies published in the literature demonstrate the use of traditional machine learning methods to provide segmentation for the brain tumor, but not classification or grading. For example, the Bayesian Model used by [PERSON] et al. was for the purpose of detecting and segmenting brain tumor from adjacent edema in multichannel MR 3D scans [20]. This Bayesian model was demonstrated to be computationally efficient with a segmentation accuracy of up to 88% compared to earlier studies analyzing MR images [21; 22; 23; 24]. This model also had the second shortest computation time as compared to the previous studies [21; 22; 23; 24]. Additional studies used expectation-maximization for a fully-automated tumor segmentation [25]. Expectation-maximization estimates the Probability Density Functions (PDFs) of the brain tissue classes and the intensity heterogeneity based on using T1- and T2-weighted MR images. The results of their approach were compared with a manual and semi-automatic approaches, where it gave a comparable, but less accurate performance as compared to the performance in 3D object segmentation of brain tumors [26; 27]. In image analysis studies by [PERSON] the authors applied the Support Vector Machine (SVM) algorithm to classify glioma tumors [28]. They achieved a varying accuracy between 85% and 90% on the dataset they used. Both T1- and FLAIR-weighted MR images were used to assess the designed SVM classifier. The classification accuracy, sensitivity and specificity, were respectively 85%, 87% and 79% for discrimination of metastases from gliomas and 88%, 85% and 96% for discrimination of high-grade from low-grade gliomas. The study by [PERSON] et al.