## 4 Conclusions  
This study aims to identify rock types through the segmentation of thin-section rock images. Commonly used models, such as DeepLabv3+, SegFormer, ConvNext, and Mask2 Former, were evaluated to compare the performance of different segmentation models. Additionally, an improved version of Mask2 Former was analyzed to enhance its performance. After the segmentation stage, SLIC super pixel and morphological operations were used to improve the performance of Mask2 Former by more than 5%. Another significant aspect of the study is the detailed analysis of other models using the relevant dataset. This analysis involved evaluating the success rates, accuracy, and error values of the models. An important contribution of this study is its ability to accurately identify minerals using the Quartz Feldspar Plagioclase (QAP) diagram commonly used in rock identification. The results indicate that the improved version of Mask2 Former offers a distinct advantage over other models. This study significantly enhances the effectiveness of image processing techniques for rock type identification. Furthermore, the detailed comparison of different models provides valuable guidance for researchers in selecting the most suitable model. Advancements in data collection technologies within geological sciences have made rapid and automated mineral identification a necessity for engineering geology applications, such as petroleum geology, mining, earthquake zones, active/potential fault areas, landslides, and rock falls. The development of automated mineral identification methods not only accelerates the mineral identification process but also facilitates the creation of reliable data for interpreting mineral image content by taking into account microscopy, cameras, and lighting conditions. In this study, the automatic identification of minerals present in thin-section images and the classification of rocks are achieved by quantifying mineral detection through image processing. This enables the accurate identification of minerals using traditional machine learning algorithms while minimizing manual errors and allowing for the unsupervised classification of thin sections. The developed method is faster and more cost-effective compared to other analytical approaches and encompasses a real-time artificial intelligence-supported software-hardware system. However, certain limitations exist regarding the scope of the dataset used, the performance of the artificial intelligence algorithm for specific mineral types, and the generalizability of the model. The most notable limitation lies in the variability of a single mineral across different samples. For example, a plagioclase mineral in a granite rock may appear gray, partially altered, and anhedral, whereas the same mineral in a basalt rock may exhibit euhedral forms and twin lamellae. Nevertheless, such challenges can be overcome through an extended training process using larger datasets and by considering additional physical properties of minerals, such as color and cleavage. Future studies may focus on enhancing the model's accuracy by employing more diverse and larger datasets. Additionally, integrating various image processing techniques could yield more precise results, thereby addressing existing challenges and advancing the field further. Conceptualization, I.A. and A.D.K. ; methodology, I.A., A.D.K. and T.K.S. ; software, I.A. and T.K.S. ; validation, I.A. and T.K.S. ; formal analysis, I.A. and T.K.S. ; investigation, I.A., A.D.K. and T.K.S. ; resources, I.A., A.D.K. and T.K.S. ; data curation, I.A. ; writing--original draft preparation, I.A., A.D.K. and T.K.S. ; writing--review and editing, I.A. ; visualization, I.A. and T.K.S. ; supervision, I.A. and T.K.S. ; project administration, A.D.K. ; funding acquisition, A.D.K. All authors have read and agreed to the published version of the manuscript. This study was financially supported by Firat University with FUBAP-MF.24.49. Not applicable. Data Availability Statement: Data can be provided upon request.