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Image classification of Chinese medicinal flowers based on convolutional neural network
1Department of Industrial Engineering & Management, National Chin-Yi University of Technology, 57, Sec. 2, Chung-Shan Rd., Taiping, Taichung, Taiwan.
Background And Objective:
Traditional Chinese medicine has used many herbs on the prevention and treatment of diseases for thousands of years. However, many flowers are poisonous and only few herbs have medicinal properties. Relying on experts for herbs identification is time consuming. An efficient and fast identification method is proposed in this study.
Methods:
This study proposes ResNet101 models by combining SENet and ResNet101, adding convolutional block attention module or using Bayesian optimization on Chinese medicinal flower classification. The performances of the proposed ResNet101 models were compared.
Results:
The best performance for accuracy, precision, recall, F1-score and PR-AUC are coming from ResNet101 model with Bayesian optimization which are 97.64%, 97.99%, 97.86%, 97.82% and 99.72%, respectively.
Conclusions:
The proposed ResNet101 model provides a better solution on the image classification of Chinese medical flowers with favourable accuracy.
