## 4 Discussion  
The review reveals that practical contributions dominate the field, with 47 studies focusing on practical applications, compared to only six studies classified as conceptual contributions. This trend highlights a strong inclination among researchers over the past decade toward advancing practical aspects of the field rather than theoretical frameworks. Furthermore, this review identified two pivotal works [32, 44] that introduced ground-breaking methods widely recognized as innovations. These studies have garnered substantial citations, underscoring their significant impact and contributions to the field. The training and testing mechanisms discussed in this review incorporate various methods and techniques. The most frequently used approach is the one-split method, employed in 26 papers. This method involves dividing the dataset into two parts, typically allocating the majority for training and the remainder for testing (e.g., 80-20% or 70-30% splits). Dataset size and model complexity often determine the split ratio. The second most common approach is data augmentation, which has been applied in 20 papers to increase dataset size and diversity by introducing transformations to the existing data. The validation technique, used in 14 papers, divides datasets into three parts for training, validation, and testing. Additional techniques include the following:  
* Support vector machines (SVM), noted in five papers;
* MATLAB, Keras, and Z-score normalization. \begin{table}
\begin{tabular}{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
35 & [PERSON] et al., 2023 [52] & Split 67-13-20 & DICE score, accuracy, sensitivity, specificity, F1 score, precision & Cropping and converting to grayscale, binary thresholding \\ \hline
36 & [PERSON] et al., 2024 [53] & 5-fold cross-validation Dataset split into 60 training, 20 validation, 20 testing & Accuracy, Sensitivity, Specificity, Precision, ROC-AUC & classification task only (binary, multi-type, and tumor grade) \\ \hline
37 & [PERSON] et al., 2019 [54] & Split 90-0-10, cross-validation method, SVM & Accuracy, sensitivity, specificity & Wavelet denoising and histogram equalization \\ \hline
38 & [PERSON] et al., 2022 [58] & & Accuracy, specificity, precision & Otsu method \\ \hline
39 & [PERSON] et al., 2016 [59] & SVM & DICE score, accuracy & Fuzzy c-means clustering, thresholding method, Watershed segmentation \\ \hline
40 & [PERSON] \& [PERSON], 2018 [63] & Augmentation & DICE score, accuracy & CNN, wavelet transform, median filtering \\ \hline
41 & [PERSON] et al., 2022 [64] & & Accuracy, sensitivity, precision & UNET, Markov Random Field (MRF) \\ \hline
42 & [PERSON] et al., 2022 [65] & Split 80-20, cross-validation method, augmentation & DICE score, sensitivity, specificity, Hausdorff 95 distance & CNN, transforming Cartesian coordinates to spherical coordinates, Canny \\ \hline
43 & [PERSON] et al., 2023 [68] & Split 34-46 & Sensitivity, specificity, Jaccard coefficient, peak signal-to-noise ratio, mean square error (MSE) & Resnet \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._These methods were employed in 43 papers that utilized segmentation approaches to evaluate model performance. This review highlights the popularity of techniques such as dataset splitting, augmentation, and validation. They are essential steps in deep learning workflows, each with specific advantages and limitations. Splitting the dataset into training, validation, and test sets helps assess model performance objectively, but improper splitting may lead to biased results. Data augmentation enhances model generalization by creating diverse samples from limited data; however, excessive or unrealistic transformations can introduce noise. Validation techniques, such as cross-validation, improve reliability but increase computational cost.