## 3 Results
### Literature Classification  
A comprehensive qualitative analysis was conducted in response to the first research question. The following subsections describe the steps taken to complete this analysis. Figure 11: Distribution of segmentation techniques used in tumor classification studies. Figure 10: Dataset sources in tumor classification research. 3.6.1. Classification of Articles into \"Conceptual Contribution\" and \"Practical Contribution\" Groups  
Each article was classified as either a \"conceptual contribution\" or a \"practical contribution\". * A conceptual contribution refers to research that involves description, comparison, analysis, or review-based studies on the topic;
* A practical contribution indicates that the research involved designing, developing, or implementing a program or presenting a novel algorithm. This study includes 53 research papers, analyzed based on their research methodology. The articles were categorized as either conceptual or practical contributions, as shown in Table 3, which presents the classification of the 53 articles. \begin{table}
\begin{tabular}{c l l l} \hline
**No.** & **Authors** & **Approach** & **Type** \\ \hline
1 & [PERSON] et al., 2021 [17] & CNN, sliding mechanism & Practical \\ \hline
2 & [PERSON] et al., 2023 [18] & Social spider optimization (SSO) algorithm & Practical \\ \hline
3 & [PERSON] et al., 2021 [19] & U-Net, intensity normalization, spatial harmonization & Conceptual \\ \hline
4 & [PERSON] et al., 2023 [20] & Fuzzy c-means clustering & Practical \\ \hline
5 & [PERSON] \& [PERSON], 2022 [5] & K-means clustering, gray level co-occurrence matrix & Practical \\ \hline
6 & [PERSON] et al., 2020 [21] & CNN, U-Net & Practical \\ \hline
7 & [PERSON] et al., 2018 [22] & K-means clustering & Practical \\ \hline
8 & [PERSON] et al., 2024 [23] & Gray standard normalization, regional binary patterns & Practical \\ \hline
9 & [PERSON] et al., 2016 [24] & CNN, intensity normalization, bias field correction & Practical \\ \hline
10 & [PERSON] \& [PERSON], & Hybrid FCM-GMM (fuzzy c-means and Gaussian mixture model) & Practical \\ \hline
11 & [PERSON] et al., 2021 [26] & U-Net, context-guided attentive CRFs & Practical \\ \hline
12 & [PERSON] et al., 2023 [27] & Modality-correlated cross-attention (MCCA), trans and CNN feature calibration (TCFC), transformer & Practical \\ \hline
13 & [PERSON] \& [PERSON], 2021 [28] & U-Net, inversion recovery (IR) & Practical \\ \hline
14 & [PERSON] et al., 2021 [29] & GANS for knowledge transfer, cross-modality feature transition (CMFT) process, modalities, and a cross-modality feature fusion (CMFF) & Practical \\ \hline
15 & [PERSON] et al., 2023 [30] & Primary feature conservation (PFC) strategy and compact split-attention (CSA) & Practical \\ \hline
16 & [PERSON] et al., 2022 [31] & 3d U-Net & Practical \\ \hline
17 & [PERSON] et al., 2022 [32] & 3D attention U-Net, a mathematical model for pixel enhancement & Practical \\ \hline
18 & [PERSON] et al., 2021 [33] & CNN & Practical \\ \hline
19 & [PERSON] et al., 2022 [34] & CNN, transformer & Practical \\ \hline
20 & [PERSON] et al., 2016 [35] & K-means clustering, median filtering, and skull masking & Practical \\ \hline
21 & [PERSON] et al., 2021 [36] & CNN & Practical \\ \hline
22 & [PERSON] et al., 2022 [37] & Neuroxi framework & Conceptual \\ \hline
23 & [PERSON] et al., 2021 [38] & U-Net & Practical \\ \hline \end{tabular}
\end{table} 
Table 3: Article classification.