## 3 Results
### Literature Classification
\begin{table}
\begin{tabular}{c l l l} \hline \hline
**No.** & **Authors** & **Approach** & **Type** \\ \hline
24 & [PERSON] et al., 2023 [39] & Canny Mayfly algorithm (ACMA), spatial gray level & Practical \\  & & dependence matrix (SGLDM) & \\ \hline
25 & [PERSON] et al., 2021 [40] & CNN & Practical \\ \hline
26 & [PERSON] et al., 2021 [41] & HPU-Net, fuzzy c-means clustering & Practical \\ \hline
27 & [PERSON] et al., 2021 [42] & U-Net & Practical \\ \hline
28 & [PERSON] et al., 2022 [43] & CNN & Practical \\ \hline
29 & [PERSON] \& [PERSON], 2023 [44] & U-Net & Practical \\ \hline
30 & [PERSON] et al., 2020 [45] & Context residual module, inter-slice context information & Practical \\ \hline
31 & [PERSON] et al., 2021 [46] & CNN, Chan–Vese algorithm & Conceptual \\ \hline
32 & [PERSON] et al., 2024 [47] & Fuzzy c-means clustering, adaptive contrast & Practical \\  & & enhancement algorithm (ACEA), and a median filter & Practical \\ \hline
33 & [PERSON] et al., 2021 [48] & Teacher model with adversarial learning, signed distance & Practical \\  & & maps (SDM) & Practical \\ \hline
34 & [PERSON] et al., 2019 [49] & The paper does not focus on image segmentation but utilizes a principal component analysis-normalized GIST (PCA-NGIST) feature extraction method without segmentation. & Practical \\ \hline
35 & [PERSON] \& [PERSON], 2020 [50] & FAHS-SVM, skull stripping, morphological operations, and wavelet transformation & Practical \\ \hline
36 & [PERSON] et al. (2024) [51] & ResNet-50 and Stacked Autoencoders & Practical \\ \hline
37 & [PERSON] et al., 2023 [52] & Cropping and converting to grayscale, the binary thresholding method & Practical \\ \hline
38 & [PERSON] et al., 2024 [53] & Deep Convolutional Neural Network (CNN) & Practical \\ \hline
39 & [PERSON] et al., 2019 [54] & Otsu method wavelet denoising and histogram equalization & Practical \\ \hline
40 & [PERSON] et al., 2019 [55] & The paper reviews multiple approaches, focusing on a hybrid model combining fully convolutional neural networks (FCNN) with conditional random fields (CRF) & Conceptual \\ \hline
41 & [PERSON], 2023 [56] & The paper focuses on classification, not segmentation, but employs image preprocessing techniques such as resizing, grayscale conversion, image smoothing, and enhancement prior to classification & Practical \\ \hline
42 & [PERSON] et al., 2020 [57] & Binary segmentation & Practical \\ \hline
43 & [PERSON] et al., 2022 [58] & Otsu method & Practical \\ \hline
44 & [PERSON] et al., 2016 [59] & Fuzzy c-means clustering, thresholding method, Watershed segmentation & Practical \\ \hline
45 & [PERSON] \& [PERSON], & Filters & Practical \\  & 2023 [60] & The segmentation aspect is not the focus of the paper. & Practical \\ \hline
46 & [PERSON] \& [PERSON], 2019 [61] & Instead, it discusses image enhancement techniques to improve the quality of brain MRI images & Practical \\ \hline
47 & [PERSON] \& [PERSON], & N4 ITK algorithm & Conceptual \\  & 2017 [62] & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._The practical contribution category represents the majority, with 47 articles, while the conceptual contribution category consists of only six studies. This suggests that researchers focus more on developing practical applications than conceptual or theoretical discussions.