# A Novel Deep Learning Method for Recognition and Classification of Brain Tumors from MRI Images
## 2 Related Work  
Due to the high clinical importance and the complex nature of the brain tumor, the advancement of an automatic model is an active research area. This section briefly discusses the relevant works for brain tumor classification and segmentation from MR images. Initially, the ML approaches such as support vector machines vector machines [19], conditional random forests [20], decision forests [21], principal component analysis [22], and fuzzy c-means [23] were presented. A common aspect of these methods is that they classify image voxels based on the pre-defined feature set known as handcrafted features, which require a human expert to figure out the most promising features from training images to start the training process. In recent years, DL-based approaches have exhibited encouraging results in the automatic segmentation of medical imaging [25]. The most important aspect of DL approachesis that they can learn complex feature representation automatically from the training data and thus result in a more robust feature vector. Various DL models for the automatic recognition of tumors have been presented, achieving promising results [30; 31; 32; 33; 34; 36; 44]. [PERSON] et al. [30] trained two different 2D CNN with deeper layers as a sliding window classifier for the segmentation of both LG and HG glioblastomas. [PERSON] et al. [45] proposed a two pathway-based 2D CNN approach on large patches to incorporate both global and local information. A local path focuses on the information in neighbor pixels while a global path captures larger contextual information simultaneously from MRI. [PERSON] et al. [32] introduced a 3D CNN architecture that considers 3D patches along with global contextual features through down-sampling, as post-processing, a fully connected CRF network was employed. In [46], the extended form of DeepMedic by adding residual connection is proposed for tumor segmentation. These above-mentioned approaches built using CNN for brain tumor segmentation operate at patch-level and consider local regions in MRI images which are then used to classify each patch [47]. Based on the obtained classification results, the central pixel is labeled; thus, it only explores spatially limited contextual information. Recently, FCNNs have achieved promising results for the segmentation of natural images [35] as well as medical images [36]. In FCNN, convolutional kernels are employed instead of fully connected layers. The original size of the image is restored by using up-sampling and deconvolution layers. Moreover, the model is trained end-to-end and has computational efficiency over patch-level classification approaches. [PERSON] et al. [31] proposed an architecture, named InputCascadeCNN, which passes pixel-wise probability estimates obtained by first CNN as an additional input to the following one. This two-stage training strategy solves an imbalance of label distribution and captures multi-scale features using a multi-cascaded network. [PERSON] et al. [33] presented a unified framework by integrating FCNNs with CRFs, such that the results reported after the segmentation of tumors maintain their appearance and spatial consistency. In [28], a multi-cascade convolutional neural network was proposed that takes both local pixel dependencies and more discriminative multi-scale features of 3D MRI images into account. To further refine the obtained results, CRFs are used to smoothen the tumor edges and eliminate false positives. In [36], authors developed the U-Net an encoder-decoder-based mod which consists of a regular FCNN followed by a contracting path capturing contextual features by down-sampling at each layer and an expanding path which raises the image size by up-sampling at each layer, thus enabling precise localization and segmentation, and it is well suited for the segmentation of medical imaging [48]. [PERSON] et al. [34] adopted a U-net CNN architecture for the segmentation of a tumor with some minor modifications.