## 3 Results
### Literature Classification
#### 3.6.3 Studying the Train/Test Mechanism, Evaluation Metrics, and Segmentation Approaches
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**No.** & **Authors** & **TT Mechanism** & **Evaluation Metrics** & **Segmentation Approach** \\ \hline
1 & [PERSON] et al., 2021 [17] & Split 80-20, cross-validation method & DICE score, predicted tumor type accuracy score, and sensitivity & CNN, sliding mechanism \\ \hline
2 & [PERSON] et al., 2023 [18] & Split 70-30, cross-validation method & Accuracy, sensitivity, specificity, F1 score & Social spider optimization (SSO) algorithm \\ \hline
3 & [PERSON] et al., 2021 [19] & Augmentation & Hausdorff distance, volumetric mismatch & U-Net, intensity normalization, spatial harmonization \\ \hline
4 & [PERSON] et al., 2023 [20] & MATLAB TOOL, & Pixel accuracy, accuracy, sensitivity, specificity, error rate & Fuzzy c-means clustering \\ \hline
5 & [PERSON] \& [PERSON], 2022 [5] & Split 70-30 & DICE score, accuracy, Sensitivity, specificity & K-means clustering, Gray level co-occurrence matrix (GLCM) \\ \hline
6 & [PERSON] et al., 2020 [21] & Split 60-40 & DICE score, accuracy & CNN, U-Net \\ \hline
7 & [PERSON] et al., 2018 [22] & & DICE score, accuracy, precision, recall, true positive (TP), true negative (TN), false positive (FP), false negative (FN) & K-means clustering \\ \hline
8 & [PERSON] et al., 2024 [23] & & False classification ratio, accuracy, Sensitivity, specificity & Gray standard normalization, regional binary patterns \\ \hline
9 & [PERSON] et al., 2016 [24] & Cross-validation method & DICE score, Sensitivity, Positive Predictive Value (PPV) & CNN, intensity normalization, bias field correction \\ \hline
10 & [PERSON] \& [PERSON], 2023 [25] & Split 70-30 & Accuracy, sensitivity, specificity, precision, recall, F-score, DICE Similarity Index (DSI) & Hybrid FCM-GMM (fuzzy c-means and Gaussian mixture model) \\ \hline
11 & [PERSON] et al., 2021 [26] & Cross-validation method & Sensitivity, specificity, Hausdorff 95 distance & U-Net, context-guided attentive CRFs \\ \hline
12 & [PERSON] et al., 2023 [27] & Split 80-10-10 & DICE score, sensitivity, Hausdorff 95 distance & Modality-correlated cross-attention (MCCA), Trans and CNN feature calibration (TCFC), transformer \\ \hline
13 & [PERSON] \& [PERSON], 2021 [28] & Split 80-20 & DICE score, accuracy, sensitivity, specificity, Jaccard index, Matthews correlation coefficient (MCC), and area under the curve (AUC) & U-Net, inversion recovery (IR) \\ \hline
14 & [PERSON] et al., 2021 [29] & Split 80-20 & DICE, score, sensitivity, specificity, Hausdorff 95 distance & Gans for knowledge transfer, cross-modality feature Transition (CMFT) process, modalities, and a cross-modality feature fusion (CMFF) \\ \hline
15 & [PERSON] et al., 2023 [30] & Split 70-10-20, augmentation, Adam optimization & DICE score, accuracy, precision, recall, mean intersection over union (MIOU) & Primary feature conservation (PFC) strategy and compact split-attention (CSA) \\ \hline
16 & [PERSON] et al., 2022 [31] & Split 80-20, augmentation & DICE score, ICC & 3D U-Net \\ \hline
17 & [PERSON] et al., 2022 [32] & Split 77-23 & Sensitivity, specificity, Hausdorff65 distance & 3D attention U-Net, a mathematical model for pixel enhancement \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of training/testing techniques, evaluation metrics, and segmentation approaches in brain tumor segmentation studies.