A Photomicrographic Dataset of Rocks for the Accurate Classification of Minerals.

ABSTRACT: Automated mineral identification in thin-section petrography remains challenging due to limited datasets capturing complete optical characteristics across crystallographic orientations. The Menoufia University Machine Learning Dataset for Minerals Classification 2025 (MUMDMC2025) provides 14,400 high-resolution photomicrographs of five mineral classes: Biotite, Hornblende, Plagioclase, Potassium-Feldspar, and Quartz from Egyptian Eastern Desert granite samples. Each mineral specimen was systematically imaged at 72 rotational positions (5° increments, 360° coverage) under both Plane Polarized Light (PPL) and Cross Polarized Light conditions (XPL), documenting complete anisotropic optical properties including pleochroism, birefringence, and extinction patterns. This comprehensive rotational

SUBMITTER: Amer BG 

PROVIDER: S-EPMC12606340 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature