## 3 Results
As for the best model, the MobileNet model emerged as the best, achieving the highest overall precision across most minerals, with values such as 0.998 for Aragonite and 0.997 for Gypsum, combined with strong recall and F1 scores (e.g., 0.996 for Aragonite, Obsidian, Quartz, and Gypsum). The MobileNet model was followed by the Xception model, which showed competitive performance, with high precision for minerals such as Hematite (0.999) and Aragonite (0.996), although it showed slightly lower recall for Obsidian (0.964) compared to the MobileNet model and other minerals. The VGG16 model has a slightly lower performance but consistently exhibits high precision, recall, and F1 scores, such as 0.997 for Hematite and 0.992 for Pyrrite. Statistical comparisons of model accuracy revealed significant differences in performance, as determined by the Wilcoxon signed-rank test. For all pairwise comparisons--VGG16 vs. MobileNet, VGG16 vs. Xception, and MobileNet vs. Xception--the Wilcoxon statistic was 0.0, with \(p\)-values of 0.0020, 0.0076, and 0.0114, respectively. These \(p\)-values, all below the 0.05 threshold, confirm that the observed differences in accuracy are statistically significant. The Wilcoxon statistic of 0.0 indicates that, across all folds, one model consistently outperformed the other without exceptions. Specifically, MobileNet demonstrated superior accuracy compared to VGG16, while Xception also outperformed VGG16. Additionally, MobileNet slightly surpassed Xception in overall performance. These findings underscore the effectiveness of MobileNet and Xception for the classification task, with MobileNet emerging as the best-performing model overall. Concerning the implementation of the Grad-CAM, the heatmaps illustrated in Figure 4 on the top row display the mineral Aragonite, characterized by its sharp crystal formations. The VGG16 model primarily focuses on the central crystalline structure but shows dispersed attention to surrounding regions, achieving a probability of 0.9415, indicating good confidence but slightly lower precision compared to the other models. The Xception model concentrates on the most prominent features, particularly the central and top regions  
Figure 3: (**a**) Loss and (**b**) accuracy curves per epoch. of the crystal, with a high probability of 0.9994, demonstrating precise feature extraction and strong confidence in classification. The MobileNet model, similar to Xception, focuses on the central structural regions but with a slightly broader spread, achieving a probability of 0.9984, reflecting high confidence with a balanced approach between precision and broader context. The second row features Gypsum, known as desert rose, a mineral characterized by its distinctive rosette-like structure and fine patterns. VGG16 highlights the core rosette structure but extends attention to less relevant peripheral areas, achieving a high probability of 0.9998 despite its less precise focus. Xception concentrates almost exclusively on the central spiral patterns of the rosette, demonstrating exceptional precision and achieving a perfect probability. MobileNet also focuses effectively on the rosette structure, though its attention is slightly less concentrated than Xception's, and it matches Xception's confidence with a perfect probability, providing a balanced approach between precision and broader focus. The third row features Obsidian, a mineral known for its darker features and smooth texture. VGG16 focuses broadly on the center of the mineral but also highlights peripheral areas, resulting in less precision, with a probability of 0.9974, indicating strong but slightly reduced confidence compared to the other models. Xception precisely targets the core textural features and key edges of the mineral, achieving a probability of 0.9836, slightly lower than expected but still highly confident.