## 1 Introduction
The annotations of different tissues were conducted by 3-5 experts. The contributions made by this paper refer to the experiments carried out on the Amazon Sagemaker via applying the implemented six deep learning convolutional neural network architectures. We assess the advantages and disadvantages of these architectures by applying them onto the BraTS 2020 Brain Tumor Segmentation Challenge Database. We compare our results to the best results obtained at this competition. In addition to the experimentally determined hyperparameter setup, we also apply the hyperparameter optimization framework offered by the Sagemaker system to determine the adequate values for the CNN hyperparameters. The methods before the era of deep learning use several basic image processing techniques that apply supervised, semi-supervised, or unsupervised methods. The main methods include thresholding-based methods, region-based methods, and pixel classification methods [7]. Thresholding methods include global and local thresholding. Region-based methods include region growing, watershed, fuzzy c-means clustering, active contour, and so on. Pixel segmentation methods are generally model-based methods such as level set, Markov random fields, self-organizing maps, model-based fuzzy classification, genetic algorithms, support vector machines, artificial neural networks, and one of the best including random forest [8; 9; 10]. The research field of deep learning algorithms began in recent years, starting in 2012-2013. Deep neural networks require many input images and a high computational capacity executed on the most up-to-date GPU cards. Training consists of an optimization procedure that relies on a well-established deep neural network architecture, adequate weight initialization, well-chosen hyperparameters such as optimization algorithm, loss functions, learning rate, and so on. In the beginning, deep learning methods in the literature were developed for object detection and image segmentation. Since 2015-2016, the deep learning strategy has been applied in several medical applications: cell segmentation by UNet [11], prostate segmentation by VNet [12], and 3D UNet [13] kidney segmentation in volumetric data. The most recent MRI brain tumor segmentation methods were published and summarized during the MICCAI BRATS Challenge [14]. Since 2016, almost every paper published has been based on the deep learning strategy. In the literature, deep learning methods are classified in the following three categories [15]: convolutional neural networks, recurrent neural networks, and Generative Adversarial Networks, while a fourth category considers an ensemble or a combination of several architectures. Convolutional neural networks are based on the convolution operation between layers followed by pooling for halving the input dimension activation, layer and finally the fully connected layer. These types of CNNs are divided into single-path and multi-path CNNs. Single-path CNNs build only a single path from the input to the output [16; 17]. The most important disadvantage of single-path CNNs is the single scale, which is zoomed out from layer to layer. The multi-path CNNs can extract different features from different resolutions, considering multi-pathways of architecture. Multi-pathway CNNs consider a local and global pathway. The local pathways consider small-size kernels, and the global pathways take kernels of a larger size into account. Multi-pathway CNNs can also be implemented through multiple input path size resolution [18; 19]. Here, they include the FCN--fully convolutional neural network--where the fully connected layers from the CNNs are replaced by deconvolution layers. These layers can up-sample the down-sampled innermost layer to a higher resolution by gradually doubling from layer to layer until the original size is reached. The most important bottleneck of these encoder-decoder networks is the lack of accurate boundary detection. In [20], a boundary-aware fully connected CNN was proposed.