## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Mineral Type**} & \multicolumn{4}{c}{**Mask2 Former + Refined**} & \multicolumn{4}{c}{**Mask2 Former**} \\ \cline{2-7}  & **P** & **R** & **F1** & **P** & **R** & **F1** \\ \hline
1 & **0.95** & **0.95** & **0.95** & 0.89 & 0.77 & 0.82 \\ \hline
2 & **0.98** & 0.98 & **0.98** & 0.87 & **1.00** & 0.93 \\ \hline
3 & **1.00** & **0.60** & **0.75** & 0.33 & 0.40 & 0.36 \\ \hline
4 & **1.00** & **1.00** & **1.00** & 0.00 & 0.00 & 0.00 \\ \hline
5 & **0.90** & **0.84** & **0.87** & 0.79 & 0.67 & 0.68 \\ \hline
6* & **0.79** & **0.73** & **0.76** & 0.69 & 0.54 & 0.60 \\ \hline
7* & **0.89** & **1.00** & **0.94** & 0.71 & 0.65 & 0.68 \\ \hline
8* & 0.86 & **0.91** & 0.88 & 0.80 & 0.91 & 0.85 \\ \hline
9* & 0.53 & **0.41** & **0.47** & 0.20 & 0.19 & 0.19 \\ \hline
10* & 0.73 & 0.67 & **0.70** & 0.52 & 0.71 & 0.60 \\ \hline
6 & **0.62** & **0.71** & 0.66 & 0.41 & 0.38 & 0.40 \\ \hline
7 & 0.80 & 0.95 & 0.88 & 0.66 & 0.86 & 0.75 \\ \hline
8 & **0.79** & **0.73** & **0.76** & 0.53 & 0.60 & 0.56 \\ \hline
9 & **0.61** & **0.82** & **0.70** & 0.46 & 0.65 & 0.54 \\ \hline
10 & **1.00** & **0.65** & **0.79** & 1.00 & 0.48 & 0.65 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Metrics for mineral identification using QAP diagram. Figure 10: Confusion matrix of two models: (**a**) Mask2 Former and (**b**) Mask2 Former+refined. According to the metrics provided in Table 6, mineral recognition performance is notably more accurate for Mask2 Former+Refined. However, due to the low occurrence of certain minerals in the dataset, the metrics for these minerals might be lower. Notably, the mineral recognition accuracy for Mask2 Former+Refined reached 85.7%, whereas it was 72% for Mask2 Former. Furthermore, the proposed approach was compared with existing studies in the literature. To our knowledge, no similar approach has been proposed in the literature for the specific rock types utilized in this study. While some studies have employed segmentation approaches to identify easily distinguishable rock types such as sandstone, no comparable methodologies have been applied to the rock types examined in our study. Table 7 presents a comparison of our proposed approach with studies from the literature. From the comparison results given in Table 7, it can be seen that the rock images selected for segmentation are mostly sandstone images. These rock types are simple in structure and the minerals are quite easy to distinguish. To the best of our knowledge, no such comprehensive analysis on thin-section rock images has been conducted in the literature. In [PERSON] [20], a segmentation approach based on Resnet18 backbone, DeepLabV3+ model and VGG16 was used for thin-section sandstone images. Prediction values were obtained for a total of 10 classes and it was experimentally proven that DeepLabV3+ gave better results. In [PERSON] [22], an edge detection-based approach is presented for mineral detection in thin-section images, which is an important step in petrographic image analysis. With the proposed multi-scale edge detection, the size of mineral grains is measured and knowledge about the composition of the rock is obtained. The samples in the dataset used are quite suitable for edge detection and the MIoU value is calculated as 75% for edge extraction only. In [PERSON] and Tahmasebi [23], a SegNet-based approach is proposed for the segmentation of grayscale Berea sandstone images. The images used are in grayscale and the number of classes is limited to five. In addition, the number of images used for deep learning is only twenty samples, which is not sufficient for the performance