A Performance Evaluation of Microscopic Geological Datasets for Mineral Classification Using Machine and Deep Learning Techniques | ||
| IJCI. International Journal of Computers and Information | ||
| Articles in Press, Accepted Manuscript, Available Online from 30 October 2025 | ||
| Document Type: Original Article | ||
| DOI: 10.21608/ijci.2025.379424.1195 | ||
| Authors | ||
| Badr G. Amer* 1; Hamdy M. Mousa2; Maher Dawoud3; Anas Youssef4 | ||
| 1CS Dept., Faculty of Computers and Artificial Intelligence, Matrouh University | ||
| 2CS Dept., Faculty of Computers and Information, Menoufia University | ||
| 3Geology dep., faculty of science, Menofia University | ||
| 4Computer Science, Faculty of Computers and Information, Menoufia University | ||
| Abstract | ||
| Classifying minerals from thin-section microscopic rock images plays a vital role in modern geoscience, supporting tasks ranging from petrographic analysis to mineral exploration and educational applications. While recent advances in machine and deep learning have enabled significant progress in automated image classification, the success of these models depends heavily on the quality and sufficiency of the training datasets. Despite the availability of several geological image datasets, questions remain about whether these datasets are adequate to support robust, generalizable artificial intelligence models in real-world geological contexts. This paper addresses a critical yet underexplored issue in this field, which is the sufficiency of the currently available thin-section image datasets for training reliable classification models. To investigate this issue, a set of widely used machine and deep learning models, namely: KNN, DT, SVM, CNN, and ANN, were applied to selected geological datasets, namely: Igneous and Metamorphic Dataset, Sedimentary Dataset, and Thin Section-1 Dataset, under fair experimental conditions. The results, which ranged from 3.4% to 73.7% accuracy, reveal notable limitations in the generalization performance of these models across different datasets, pointing to issues such as class imbalance, low image quality, and limited class diversity. The findings highlight the urgent need for developing a more comprehensive, balanced, and high-resolution geological dataset that can effectively support automated mineral classification systems. This study contributes to the foundational understanding of dataset sufficiency in geological AI applications and sets the stage for future work in dataset construction and model optimization. | ||
| Keywords | ||
| Mineral Classification; Microscopic Rock Images; Geological Datasets; Machine Learning; Deep Learning | ||
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