## 5 Conclusions  
This study highlights the effectiveness of a hybrid approach that combines HCC for efficient region-of-interest detection with pre-trained CNNs for accurate and robust mineral classification. Among the models evaluated, MobileNet stood out as the top performer, achieving the highest accuracy (98.98%), the lowest loss (0.0202), and excellent precision and recall across all mineral classes. Xception also performed well, delivering high accuracy (98.69%) and precise feature extraction, while VGG16, despite its comparatively lower accuracy (96.51%), proved reliable for tasks requiring detailed feature analysis. Grad-CAM visualizations provided deeper insights into the models' feature extraction capabilities, demonstrating their ability to capture distinctive visual traits of minerals such as Aragonite, Gypsum, Obsidian, and Pyrite, with MobileNet effectively balancing precision and contextual focus. The stacking ensemble approach further enhanced classification performance, achieving an impressive accuracy of 99.71% by combining the complementary strengths of the base models. This method demonstrated exceptional robustness, particularly for challenging mineral classes, with notable improvements in precision and recall over individual models. However, the computational demands of the stacking ensemble, coupled with its impact on inference time, pose challenges for real-time applications, especially on resource-constrained devices. Despite these strengths, limitations such as the lack of dataset diversity, persistent false positives, and difficulty in detecting visually similar minerals highlight areas for refinement. Environmental factors like lighting variability and occlusions further challenge real-world performance. Future work should focus on expanding and diversifying the training dataset, optimizing model architectures, and employing advanced techniques such as transfer learning and edge computing to improve scalability and efficiency. The deployment of the methodology in Mineral Quest, an interactive Python-based educational game, showcases the practical application of machine learning and image recognition for real-time mineral classification, providing an engaging and educational introduction to geology through innovative gameplay mechanics. By addressing these challenges, the proposed system has significant potential for applications in mining operations, geological surveys, and educational tools where real-time, accurate mineral classification is essential.  
**Author Contributions:** Conceptualization, P.T., IL. and M.P.; methodology, P.T.; formal analysis, P.T. and IL; data curation, P.T., IL. and M.P.; writing--original draft preparation, P.T. and IL; writing--review and editing, P.T., IL, M.P., N.S. and G.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** Funded by the project \"3 DRETURN, TTP4-0359953: GAIO-ORAMA: MINERAL RAW MATERIALS, DIGITAL CULTURE AND CIRCULAR ECONOMY\", MIS 5217186 of the Action \"Synergies of Research and Innovation in the Region of Attica\" in the framework of the Operational Program \"Attica 2014-2020\".  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The code presented in this study might be available on request from the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.