Deep learning based identification of rock minerals from un-processed digital microscopic images of undisturbed broken-surfaces - M.A. Dalhat; Sami A. Osman (Artificial Intelligence in Geosciences, 2025)
This study employed convolutional neural networks (CNNs) for the classification of rock minerals based on 3179 RGB-scale original microstructural images of undisturbed broken surfaces. The image dataset covers 40 distinct rock mineral-types. Three CNN architectures (Simple model, SqueezeNet, and Xception) were evaluated to compare their performance and feature extraction capabilities. Gradient-weighted Class Activation Mapping (Grad-CAM) was employed to visualize the features influencing model predictions, providing insights into how each model distinguishes between mineral classes. Key discriminative attributes included texture, grain size, pattern, and color variations. Texture and grain boundaries were identified as the most critical features, as they were strongly activated regions by the best model. Patterns such as banding and chromatic contrasts further enhanced classification accuracy. Performance analysis revealed that the Simple model had limited ability to isolate fine-grained details, producing broad and less specific activations (0.84 test accuracy). SqueezeNet demonstrated improved localization of discriminative features but occasionally missed finer textural details (0.95 test accuracy). The Xception model outperformed the others, achieving the highest classification accuracy (0.98 test accuracy) by exhibiting precise and tightly focused activations, capturing intricate textures and subtle chromatic variations. Its superior performance can be attributed to its deep architecture and efficient depth-wise separable convolutions, which enabled hierarchical and detailed feature extraction. Results underscores the importance of texture, pattern, and chromatic features in accurate mineral classification and highlights the suitability of deep, efficient architectures like Xception for such tasks. These findings demonstrate the potential of CNNs in geoscience research, offering a framework for automated mineral identification in industrial and scientific applications.