## 3 Results
This can be completed if the five-class segmentation is divided into four times applied binary segmentation (Background-Healthy; Healthy-Whole Tumor; Edema-Tumor Core; Enhanced Tumor-Necrosis/Non-Enhanced Tumor). This multiple binary classification pipeline will surely bring considerable improvement because the class imbalance is eliminated and, the loss is computed not based on a mean loss of all the classes, but considering the two relevant classes at each binary classification phase. In Table 2, we measured the Dice scores for background voxels, healthy voxels, and the Whole Tumor (classes \(2/4+3+5\)). The detection of background and healthy voxels is as expected. Background Dice is between 99.29 and 99.7%, and for healthy voxels, the Dice is between 95.99 and 97.46%. The detection of tumor voxels is about 90% (88.89-90.66%), which is a good result compared to the BraTS Challenge WT (Whole Tumor) average of 82.74%. The average WT Dice score of the BraTS Challenge is based on the validation table results, namely, the Validation Leaderboard [14]. The best result on the leaderboard had a Dice coefficient of 92.45%, and 80 participants out of a total of 291 are above 90%. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Architecture** & **Epoch** & **Validation Mean Accuracy** \\ \hline FCN-RN50 & 86 & 0.9516 \\ FCN-RN101 & 80 & 0.9576 \\ PSP-RN50 & 100 & 0.9465 \\ PSP-RN101 & 74 & 0.9604 \\ DeepLab-RN50 & 49 & 0.9243 \\ DeepL-RN101 & 66 & 0.9479 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Validation Accuracy of the Training Processes. Table 3 shows the results of the six different architectures on the three types of tumor tissue: edema (ED), active tumor (AT), and necrotic and non-enhanced tumor (NEC/NET). Both the edema and active tumor were about 70%. The worst results of 38-47% were obtained on the NEC/NET tissue type. In Tables 1 and 2, ResNet101 is slightly better than the ResNet50 architecture. The best results were obtained by DeepLab-ResNet101 for AT (73.7%) and NEC/NET (47.63%) and PSP-ResNet101 for ED (75.21%). Figure 10 shows some segmentation results for the visual comparison of different tumor types and the six architectures studied. On average, this barely visible difference is around 1-2%. There is no quantitative evidence clearly showing that one architecture is better than all the others. In different images, the other architecture outstrips the rest. This led to the idea of combining them into an ensemble model. The second group of experiments was related to hyperparameter optimization to possibly extract the best architecture from it. The training jobs and hyperparameter optimization setup that had to be defined in the Amazon SageMaker framework were related to the six types of CNN networks, their hyperparameters, and the input database type. The data were provided in pipe mode stored in an S3-bucket. These data were accessed via an AugmentedManifestFile containing the path to every image and to every corresponding annotation file along with the training job name and other metadata. The validation and test data were provided in the same way. The hyperparameter tuning job was run on an ml.p3.2 xlarge system on three instances. Every instance was set to run a maximum of five parallel training jobs. The maximum duration per training job was set to 48 h.  
SageMaker hyperparameter tuning uses Grid Search and Bayesian Search [54] to obtain the best set of parameters. The tuning algorithm for SageMaker performs guesses as to which sets of hyperparameters are likely to achieve better results and runs the training jobs with those parameters. The training job is abandoned before the preset number of epochs if another training job had better results regarding the objective metric in the same iteration. The objective metric was the mIOU (mean intersection over reunion). IOU computes the intersection over reunion between the predicted segmentation and the ground truth for every image (Equation (7)).