A novel Chinese herbal medicine classification approach based on EfficientNet
Wangli Hao, Meng Han, Hua Yang, Fei Hao, Fuzhong Li
Abstract
Chinese Herbal Medicine (CHM) classification is a promising research issue in Intelligent Medicine. However, the small available Chinese Herbal datasets and the traditional CHM classification model lead to huge challenge for obtaining the promising classification results. To tackle the above challenges, a novel large CHM classification (CHMC) dataset has been firstly established, which includes 100 classes with about 10,000 samples. This dataset contains a wide range of medicinal materials and natural background. Further, the promising EfficientNetB4 model is proposed to perform the CHM classification. EfficientNet can uniformly scales up the depth, width and resolution of the model, which will obtain better accuracy as it balance all dimensions of the network, including depth, width, and resolution, respectively. To validate the superiority of the EfficientNet and the effectiveness of CHMC dataset, extensive experiments have been conducted, verifying that the EfficientNetB4 is optimal for CHM classification, with 5% improvement of the existing model. In addition, this model has achieved state-of-the art CHM classification performance, with TOP-1 accuracy of 83.1%, and TOP-5 accuracy of 92.50%.