## 5 Conclusions  
The results and experiments presented above describe an automatic brain tumor segmentation approach based on the tools in AWS Sagemaker for building CNN networks and hyperparameter optimization techniques. In our results, we compare three different architectures: FCN, PSP, and DeepLab encoder-decoder networks, each with two encoder versions (ResNet50 and ResNet101). For the encoder part, we loaded in the ImageNet weights to use transfer learning. The encoders were subsequently trained, and the decoders were trained from the beginning. In our paper, we fine-tuned the hyperparameters and applied the advantages of transfer learning to obtain segmentation results on the BraTS database with the purpose of brain tumor segmentation. The system can be a very helpful tool for physicians and neurosurgeons. Brain MRI examination and a possible introduction of a regular MRI screening once every 2 years on the whole population or on the risk-patients is becoming widespread. This automated system can give an 80-90% exact segmentation response and can indicate to patients who really have suspicious cells on their MRI record to consult a doctor. The tumor can be detected even in an incipient phase, preventing HGG tumors if possible. This type of frequent examination would discover the LGG tumors in initial phases. The results obtained are comparable to the BraTS Challenge Leaderboard results and are among the first quartile in ranking. The best Dice scores we have obtained for WT are about 90%, TC 84%, and ET 78%.  
The purpose of this research was to create an end-to-end system suitable for multi-modal brain tumor segmentation that is able to differentiate the whole tumor, tumor core, healthy tissue, and background. We have adapted six different convolutional neural networks known in the literature. In our experiments, we demonstrated that these simple architectures with few parameters and a good hyperparameter setup can achieve similar results to the best ones competing in BraTS Challenges. In these architectures, the transfer learning techniques were used, and in this way, the CNNs have been trained with much fewer epochs. The training and testing phases could be conducted in less time with a smaller budget. This permits retaining and fine-tuning the current performances to include different MRI multi-modal brain datasets acquired over time in clinical environments.  
Conceptualization, S.L. and L.L.; methodology, S.L. and L.L.; software, S.L.; validation, S.L. and L.L.; formal analysis, L.S.; investigation, S.L.; resources, S.L. and L.L.; data curation, L.L.; writing--original draft preparation, S.L.; writing--review and editing, S.L., L.L. and L.S.; visualization, L.L.; supervision, L.L. and L.S.; project administration, S.L.; funding acquisition, S.L. All authors have read and agreed to the published version of the manuscript.  
The work was supported by the University of Medicine, Pharmacy, Science, and Technology \"[PERSON]\" of Targu Mures, Research Grant Number 292/4/14.01.2020.  
Not applicable.  
Data Availability Statement: The brain MRI records processed in this study are available at the BraTS website: [[https://www.med.upenn.edu/cbica/brats2021/](https://www.med.upenn.edu/cbica/brats2021/)]([https://www.med.upenn.edu/cbica/brats2021/](https://www.med.upenn.edu/cbica/brats2021/)) accessed on 27 February 2022.