## 4 Discussion  
A review of recent scientific literature highlights the transformative impact of deep learning, particularly CNNs, in significantly improving the accuracy and efficiency of mineral classification, enabling the creation of high-performance vision models tailored also for industrial applications [51; 52; 53]. [PERSON] [28] highlights in her study how CNN-based models provide accurate and efficient alternative for field geologists and mineralogists. The author reports that the implementation of CNN-based models addresses the shortcomings of traditional methods, which typically involve manual microscope examination--a process that is both time-intensive and susceptible to human error. In this study, we present a hybrid approach that combines HCC for efficient region-of-interest detection with pre-trained CNNs for precise mineral classification. This approach exploits the computational efficiency of HCC to quickly identify potential mineral regions within video frames, significantly reducing the processing load for CNNs, which then perform detailed classification. HCC are known for their fast detection capabilities, quickly \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Size (MB)** & **Training Time** & **Images Processed/s** \\ \hline VGG16 & 90.77 & 113.52 s & 16.74 \\ Xception & 210.22 & 116.97 s & 16.83 \\ MobileNet & 78.95 & 43.40 s & 44.19 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison between all models (size, training time, image processed/s). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models** & **Minerals** & **Precision** & **Recall** & **F1-Score** \\ \hline \multirow{8}{*}{Stacking Ensemble} & Aragonite & 0.998 \(\pm\) 0.001 & 0.995 \(\pm\) 0.002 & 0.996 \(\pm\) 0.003 \\  & Azurite & 0.999 \(\pm\) 0.001 & 0.996 \(\pm\) 0.003 & 0.997 \(\pm\) 0.001 \\ \cline{1-1}  & Hematite & 0.999 \(\pm\) 0.001 & 0.995 \(\pm\) 0.002 & 0.998 \(\pm\) 0.002 \\ \cline{1-1}  & Obsidian & 0.993 \(\pm\) 0.003 & 0.999 \(\pm\) 0.001 & 0.997 \(\pm\) 0.001 \\ \cline{1-1}  & Pyrite & 0.996 \(\pm\) 0.002 & 0.999 \(\pm\) 0.001 & 0.997 \(\pm\) 0.001 \\ \cline{1-1}  & Quartz & 0.996 \(\pm\) 0.001 & 1.000 \(\pm\) 0.000 & 0.998 \(\pm\) 0.001 \\ \cline{1-1}  & Gypsum & 0.997 \(\pm\) 0.001 & 0.996 \(\pm\) 0.002 & 0.996 \(\pm\) 0.002 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance metrics, precision, recall, and F1-score for the stacking ensemble. identifying potential ROIs within an image [54]. The practical implementation of this method supports real-time processing, making it highly suitable for field applications, where quick decision-making is crucial. This is particularly advantageous in mining operations, geological surveys, and educational tools, where immediate feedback can significantly impact outcomes. For the construction of the training and validation database, we also applied data augmentation techniques, including random rotations, flips, and normalization. These methods enhanced the model's exposure to diverse angles and lighting conditions, significantly improving its ability to classify minerals under varied scenarios, consistent with findings reported by other authors [40; 53]. While data augmentation proved effective in improving the model's generalization, its success is inherently tied to the availability of sufficient data to approximate the real data distribution accurately. Recent work by [PERSON] et al. [55] provides valuable insights into this relationship, introducing a theoretical learning curve for the Bayes classifier. This framework estimates the relationship between the training sample size and the excess error probability, offering a systematic approach to determine the minimum dataset size required to achieve a desired performance threshold. Although this study does not incorporate such theoretical analysis, future research could explore its application to further optimize the training dataset size.