# Segmentation of Brain Tumors from MRI Images Using Convolutional Autoencoder
## 4 Conclusions  
A new CNA architecture for brain tumor segmentation is presented in this study. Tumor segmentation was performed based on semantic segmentation, i.e., classifications of each pixel of the image into two categories, '1' for the tumor, '0' for everything else. The network architecture is simple, with less than 0.5 million parameters. The network was tested using eight tests, combinations of two evaluation methods (5-fold cross-validation and one test), two data division methods (record-wise and subject-wise), and two training datasets (original and augmented). The best segmentation result for 5-fold cross-validation and one test was achieved with the record-wise method and training the network on the augmented database. The average _Dice_ coefficient was 71.68% and 72.87% for 5-fold cross-validation and one test, respectively. There is a difference in results between the increased and the original database. The average _Acc_ classification of pixels on the original database is 99.17% and 99.22% for 5-fold cross-validation and one test, respectively, and for augmented it is 99.23% and 99.28%. To our knowledge, in the literature, no paper tested the generalization of the segmentation network by dividing the data using the subject-wise method for this image database. The best result achieved by dividing the data subject-wise was obtained by training the network on an augmented database. The mean _Acc_ classification of pixels by training the network on an augmented image database is 99.04% and 99.17% for 5-fold cross-validation and one test, respectively. The subject-wise data division results are slightly worse than with the record-wise data division, which was expected because the network has no data about the respondent during the testing. Comparing the new CNA architecture with existing ones that used the same image database and metrically presented the results was also presented. It has been shown that the proposed CNA architecture for segmentation achieves better results than the results presented in the literature. Additionally, the network has good generalization ability, and the execution time required for segmentation is quite good, with an average value of 13 ms per image, implying its effectiveness as an effective decision-support system in order to help medical workers in everyday clinical practice. In the future, our group plans to combine the already developed CNN architecture for the classification of brain tumors [3] with the presented CNA architecture for segmentation of brain tumors and adapt both networks for use in real-time and real-life conditions \begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference** & **k-Fold Cross-Validation/Data Division** & _Acc_ [\%] \\ \hline [PERSON] et al. [17] & 5-fold; 80% data in training set, 20\% in test & 95.9 \\ Proposed & 5-fold; 60\% data in training set, 20\% in validation, 20\% in test & **99.17** \\ \hline \hline \end{tabular} \(Acc\)—accuracy.  
\end{table} 
Table 4: Comparing the results of different network architectures trained on the original database and performance were tested via _k_-fold cross-validation record-wise method. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference** & **Data Division** & _Acc_ [\%] \\ \hline [PERSON] et al. [23] & 70\% data in the training set, 15\% in validation, 15\% in the test & 93.61 \\ Proposed & 60\% data in training set, 20\% in validation, 20\% in test & **99.22** \\ \hline \hline \end{tabular} \(Acc\)—accuracy.  
\end{table} 
Table 5: Comparing the results of different network architectures trained on the original database and performance were tested via one test record-wise method. during brain surgery [34], by classifying and accurately localizing the tumor. Since both architectures are small, their adaptation to working in real-time would be possible. It is planned that the developed architecture for segmentation will be tested on other medical image databases and an increased number of subjects.