Innovative application of ensemble learning strategies in automated classification of Chinese herbal medicine images
Traditional Chinese herbs are an integral part of traditional medicine,but the classification efficiency is low.To ad-dress this,this study utilized deep learning technology for automatic classification of Chinese herbal images.Several models includ-ing VGG,ResNet,Inception,and DenseNet were evaluated on a publicly available Chinese herb dataset,establishing a perfor-mance baseline.A decision-level fusion strategy was employed to weight the ensemble of predictions from these four models,signifi-cantly enhancing classification accuracy.Experimental results show that the ensemble model achieved a test set accuracy of 96.91%,substantially surpassing any individual model.This not only improves the efficiency and accuracy of Chinese herbal image classification but also paves new avenues for the application of deep learning technology in the field of traditional medicine.
Chinese herbal medicineimage classificationdeep learningmodel ensemble
万方数据
