## 3 Results
MobileNet covers the key textural features with broader attention than Xception, maintaining high relevance and achieving a perfect probability, demonstrating its strong classification capabilities even with less focused attention. The bottom row features Pyrite, a golden crystalline mineral with sharp and reflective surfaces. The VGG16 model primarily focuses on the central crystalline regions but also highlights areas outside the main structure, indicating a more generalized focus. Despite this, it achieves a perfect probability, showing strong confidence in its prediction. The Xception model exhibits sharp focus on the key structural features, particularly the reflective surfaces and crystalline edges, with a perfect probability, demonstrating precise attention and exceptional \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models** & **Minerals** & **Precision** & **Recall** & **F1-Score** \\ \hline \multirow{8}{*}{VGG-16} & Aragonite & 0.919 \(\pm\) 0.008 & 0.984 \(\pm\) 0.010 & 0.950 \(\pm\) 0.004 \\  & Azurite & 0.965 \(\pm\) 0.022 & 0.988 \(\pm\) 0.016 & 0.977 \(\pm\) 0.009 \\  & Hematite & 0.997 \(\pm\) 0.017 & 0.980 \(\pm\) 0.019 & 0.989 \(\pm\) 0.009 \\  & Obsidian & 0.988 \(\pm\) 0.010 & 0.942 \(\pm\) 0.003 & 0.965 \(\pm\) 0.003 \\  & Pyrite & 0.992 \(\pm\) 0.003 & 0.924 \(\pm\) 0.006 & 0.957 \(\pm\) 0.004 \\  & Quartz & 0.971 \(\pm\) 0.014 & 0.978 \(\pm\) 0.009 & 0.975 \(\pm\) 0.005 \\  & Gypsum & 0.959 \(\pm\) 0.004 & 0.986 \(\pm\) 0.006 & 0.972 \(\pm\) 0.004 \\ \hline \multirow{8}{*}{Xception} & Aragonite & 0.996 \(\pm\) 0.003 & 0.986 \(\pm\) 0.004 & 0.991 \(\pm\) 0.003 \\  & Azurite & 0.988 \(\pm\) 0.006 & 0.988 \(\pm\) 0.005 & 0.988 \(\pm\) 0.001 \\  & Hematite & 0.999 \(\pm\) 0.001 & 0.973 \(\pm\) 0.005 & 0.986 \(\pm\) 0.004 \\  & Obsidian & 0.932 \(\pm\) 0.005 & 0.998 \(\pm\) 0.003 & 0.964 \(\pm\) 0.003 \\  & Pyrite & 0.993 \(\pm\) 0.005 & 0.986 \(\pm\) 0.006 & 0.989 \(\pm\) 0.001 \\  & Quartz & 0.993 \(\pm\) 0.002 & 0.987 \(\pm\) 0.006 & 0.990 \(\pm\) 0.004 \\  & Gypsum & 0.990 \(\pm\) 0.002 & 0.991 \(\pm\) 0.005 & 0.991 \(\pm\) 0.003 \\ \hline \multirow{8}{*}{MobileNet} & Aragonite & 0.998 \(\pm\) 0.001 & 0.991 \(\pm\) 0.004 & 0.996 \(\pm\) 0.001 \\  & Azurite & 0.994 \(\pm\) 0.004 & 0.990 \(\pm\) 0.004 & 0.992 \(\pm\) 0.002 \\ \cline{1-1}  & Hematite & 0.994 \(\pm\) 0.002 & 0.994 \(\pm\) 0.006 & 0.994 \(\pm\) 0.003 \\ \cline{1-1}  & Obsidian & 0.993 \(\pm\) 0.005 & 0.998 \(\pm\) 0.001 & 0.996 \(\pm\) 0.003 \\ \cline{1-1}  & Pyrite & 0.991 \(\pm\) 0.005 & 0.994 \(\pm\) 0.002 & 0.993 \(\pm\) 0.002 \\ \cline{1-1}  & Quartz & 0.991 \(\pm\) 0.004 & 0.998 \(\pm\) 0.003 & 0.996 \(\pm\) 0.001 \\ \cline{1-1}  & Gypsum & 0.997 \(\pm\) 0.001 & 0.994 \(\pm\) 0.002 & 0.996 \(\pm\) 0.001 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance metrics, precision, recall, and F1-score for all models, mean values, and standard deviation. confidence. It seems that the MobileNet model balances attention between fine details and broader regions, effectively covering the main features of the mineral and achieving a perfect probability, reflecting its strong adaptability and classification capabilities. The comparison between all models highlights distinct strengths and trade-offs that make each model suitable for different applications (Table 4). In terms of size, the VGG16 model (90.77 MB) is moderately large due to its deep architecture and fully connected layers, while the Xception model (210.22 MB) was the largest, reflecting its complex structure with depth wise separable convolutions, which improve feature extraction but increase model size. The MobileNet model, at just 78.95 MB, is the smallest, making it ideal for storage-limited environments like mobile or embedded systems. Regarding training time, Xception is the slowest at 116.97 s, primarily due to its large number of parameters and computational demands, whereas VGG16, despite being lighter, trains slightly faster (113.52 s).