## 3 Results and Discussion
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Reference** & **Rock Type** & **Method** & **Performance** \\ \hline [PERSON] et al. [20] & Sandstone images & DeepLabV3+ with Resnet18 Backbone & Accuracy = 0.75 \\ \hline [PERSON] et al. [22] & Sandstone images & Edge segmentation using extinction consistency perception network & \multirow{2}{*}{MioU = 0.80} \\ \cline{1-1} [PERSON] and [PERSON] [23] & Berea sandstone images & Segnet based segmentation of grayscale images & Accuracy = 0.92 \\ \hline [PERSON] et al. [24] & Rock thin-section images & Mask-RCNN & MioU = 0.76 \\ \cline{3-4}  & Rock thin-section images & PANet Segmentation & MioU = 0.78 \\ \cline{3-4}  & & Multi-Channel Attention Transformer & \multirow{2}{*}{MioU = 0.81} \\ \cline{3-4} [PERSON] et al. [25] & Sandstone images & Smoothed score class activation mapping and restricted similarity dual contrast & \multirow{2}{*}{MioU = 0.74} \\ \cline{3-4} [PERSON] et al. [26] & Sedimentary rock thin sections & Binary grain segmentation using modified K-means++ and PM filters & Dice Score = 0.74 \\ \hline \multirow{3}{*}{**Our method**} & **Magmatic rock thin-section images** & \multirow{3}{*}{**Mask2 former+refined**} & **MioU = 0.83 Accuracy = 0.91 Mineral Identification rate = 0.85** \\ \cline{3-4} \end{tabular}
\end{table} 
Table 7: Comparison results of the proposed methods with other studies. evaluation of deep learning. In [PERSON] et al. [24], a multi-channel attention transformation approach was proposed for the segmentation of seven minerals from sandstone rocks. A copy and paste approach was used for data replication and a transformer approach with a multi-channel attention module was presented to minimize the effect of different lighting angles. Although the rock types analyzed were not complex, MIoU increases of about 5% over Mask-RCNN and 3% over PANet were achieved. In [PERSON] et al. [25], an approach combining flattened score class activation mapping with similarity double-contrast learning is applied to sandstone rock thin-section images. Their approach was also compared with class activation mapping and generative adversarial networks. Among the evaluation metrics, the average intersection over combination, pixel accuracy and false positive rate were evaluated and an MIoU value of 0.79 was obtained with 5-6% improvement compared to other methods. In [PERSON] et al. [26], different filtering approaches were used to segment grains from thin-section sediment images. The approach applied binary segmentation to extract only grain minerals from thin-section images. The Dice coefficient was used for segmentation and a performance of 0.74 was achieved. When the results of the studies in the literature are evaluated, it can be seen that the methods are generally applied to sandstone thin-section images that are easy to distinguish or focus on the detection of a small number of minerals. In this study, unlike in the literature, studies were conducted to identify both minerals and rock type from thin-section rock images. As a result of the proposed segmentation approach, studies were conducted for accuracy, MIoU and mineral identification, and more accurate results were obtained compared to in the literature. The findings of this study highlight the potential of advanced deep learning models in overcoming the limitations of traditional and existing automated methods for mineral segmentation in igneous thin rock sections. The improved Mask2 Former model demonstrated significant enhancements in segmentation accuracy, particularly in delineating complex mineral boundaries. This improvement is attributed to the integration of SLIC super pixel refinement and morphological operations, which effectively address challenges associated with heterogeneous textures and subtle boundary distinctions. Comparative analysis with other state-of-the-art models, such as DeepLabV3+, SegFormer, and ConvNeXt, underscores the robustness and versatility of the proposed approach.