## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
13 & [PERSON] \& [PERSON], 2021 [28] & X & X & X & & & X & \\ \hline
14 & [PERSON] et al., 2021 [29] & & X & X & & & X & X & \\ \hline
15 & [PERSON] et al., 2023 [30] & X & & X & X & X & & X \\ \hline
16 & [PERSON] et al., 2022 [31] & & & & & & X & \\ \hline
17 & [PERSON] et al., 2022 [32] & & X & X & & & X & \\ \hline
18 & [PERSON] et al., 2021 [33] & & & & & & X & X \\ \hline
19 & [PERSON] et al., 2022 [34] & & & & & & X & X \\ \hline
20 & [PERSON] et al., 2016 [35] & X & & & & & & \\ \hline
21 & [PERSON] et al., 2021 [36] & X & X & X & X & X & & \\ \hline
22 & [PERSON] et al., 2022 [37] & & & & & & X & \\ \hline
23 & [PERSON] et al., 2021 [38] & & X & X & & & X & \\ \hline
24 & [PERSON] et al., 2023 [39] & X & X & X & X & & & \\ \hline
25 & [PERSON] et al., 2021 [40] & X & & X & X & & & \\ \hline
26 & [PERSON] et al., 2021 [41] & & & & & & X & X \\ \hline
27 & [PERSON] et al., 2021 [42] & & & & & & X & \\ \hline
28 & [PERSON] et al., 2022 [43] & X & & & & & & X \\ \hline
29 & [PERSON] \& [PERSON], 2023 [44] & X & & X & X & X & X & \\ \hline
30 & [PERSON] et al., 2020 [45] & & & & & & & X & \\ \hline
31 & [PERSON] et al., 2021 [46] & & & & & & & X & X \\ \hline
32 & [PERSON] et al., 2024 [47] & X & X & X & & & & & X \\ \hline
33 & [PERSON] et al., 2021 [48] & & & & X & X & & & X \\ \hline
34 & [PERSON] \& [PERSON], 2020 [50] & X & X & X & & & & \\ \hline
35 & [PERSON] et al., 2023 [52] & X & X & X & X & X & X & \\ \hline
36 & [PERSON] et al., 2024 [53] & X & X & X & & X & & X \\ \hline
37 & [PERSON] et al., 2019 [54] & X & X & X & & & & \\ \hline
38 & [PERSON] et al., 2022 [58] & X & & X & & X & & \\ \hline
39 & [PERSON] et al., 2016 [59] & X & & & & X & & \\ \hline
40 & [PERSON] \& [PERSON], 2018 [63] & X & & & & X & & \\ \hline
41 & [PERSON] et al., 2022 [64] & X & X & & & X & & \\ \hline
42 & [PERSON] et al., 2022 [65] & & X & X & & & X & \\ \hline
43 & [PERSON] et al., 2023 [68] & & X & X & & & & X & X \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._area under the curve (AUC) further illustrate the wide range of evaluation methods utilized in the literature. The variety of evaluation metrics reflects the demands of different tasks and datasets. While accuracy remains a widely used metric, its limitations in addressing imbalanced datasets necessitate supplementary measures such as sensitivity, specificity, and F1 score for a more comprehensive analysis. In image segmentation tasks, metrics like the DICE score, Jaccard coefficient, and Hausdorff95 distance provide deeper insights into model performance. Domain-specific metrics such as PSNR and MSE are also critical in evaluating image quality in relevant contexts. The selection of appropriate metrics should align with the research objectives, dataset characteristics, and application requirements to ensure thorough and meaningful assessments of model efficacy. GANs, while powerful generative models, have faced several criticisms that can limit their practical application, especially when compared to more established architectures like CNNs and U-Nets for specific tasks like image segmentation. Emerging techniques like federated learning and self-supervised learning hold significant promise in the realm of tumor classification, yet they remain relatively underexplored. Federated learning offers a solution to privacy concerns by allowing multiple institutions to collaboratively train models without the need to share sensitive patient data. On the other hand, self-supervised learning is valuable in situations where annotated data are scarce. It allows models to extract meaningful patterns and representations from large volumes of unlabeled data.