关键词: 关键词: 深度学习, 细粒度图像分类, 中草药, 特征提取, 特征融合

Abstract: In the field of fine-grained image classification of traditional Chinese medicine, the lack of a comprehensive and balanced dataset has been a major obstacle. To advance research in fine-grained image recognition of Chinese herbs, the Herb-150 fine-grained Chinese herb dataset was constructed. To address the issue of deep neural networks easily losing discriminative detailed features in this task, a fine-grained feature-enhanced CHMRN(Chinese herbal medicine recognition Network) was proposed. This network introduces a top-down feature fusion module that integrates multi-scale semantic information, thereby capturing comprehensive contextual features. Additionally, a bottom-up channel information compensation module is designed to enhance the expressive power of fine-grained features, ensuring the accurate capture of subtle differences between traditional Chinese medicine categories. Experimental results show that the CHMRN achieves an accuracy rate of 93.91% on the Herb-150 dataset. Compared to fine-grained image classification models CMAL-Net and PIM,?it achieves an improvement of over 3%, while also outperforming models such as IELT, SR-GNN, I2-HOFI, and SIM-OFE. This significant improvement highlights the effectiveness of the proposed architecture in fine-grained classification tasks. The CHMRN not only improves the accuracy of traditional Chinese medicine identification, but also provides valuable references for similar fine-grained image classification applications.

Key words: Deep learning, Fine-grained image classification, Traditional Chinese medicine, Feature extraction, Feature fusion