A segmentation-combination data augmentation strategy and dual attention mechanism for accurate Chinese herbal medicine microscopic identification

Chinese Herbal Medicine (CHM), with its deep-rooted history and increasing global recognition, encounters significant challenges in automation for microscopic identification. These challenges stem from limitations in traditional microscopic methods, scarcity of publicly accessible datasets, imbalanced class distributions, and issues with small, unevenly distributed, incomplete, or blurred features in microscopic images.