An adaptive traditional Chinese herbal medicine image recognition model via ED-HLOA-optimized DenseNet201

Peiyang Wei; Rundong Zou; Guangdong Dong; Wen Qin; Jianhong Gan; Oifeng Su; Zhibin Li

doi:10.4108/airo.9563

Authors

Peiyang Wei Chengdu Technological University https://orcid.org/0009-0008-9632-8006
Rundong Zou Chengdu University of Information Technology
Guangdong Dong Beijing University of Chinese Medicine
Wen Qin Sichuan Normal University
Jianhong Gan Chengdu University of Information Technology
Oifeng Su Chengdu Technological University
Zhibin Li Chengdu University of Information Technology

DOI:

https://doi.org/10.4108/airo.9563

Keywords:

Chinese Herbal Medicine, tumors , Neural Network, Self-Adaptive Model, Image Recognition, Horned Lizard Optimization Algorithm

Abstract

Research shows that ginseng, fritillaria cirrhosa and other Chinese herbal medicines and their active components can fight tumors via immune regulation, apoptosis induction, and signaling pathway modulation. Thus, deep learning-based for authentic Chinese herbal medicine identification and classification is gaining more attention. Although convolutional neural network (CNN)-based image recognition models have made progress in CHB recognition, they often face limitations such as simple structures, fixed parameters, and a singular optimization approach, primarily relying on learning rate adjustments, which impede achieving the high accuracy required for image recognition of CHB. To address this issue, this study proposes an elite differential mutation-based horned lizard optimization algorithm (ED-HLOA) and applies it to optimize a DenseNet201-based recognition model for CHB. It enables adaptive adjustments of the learning rate and compression factor for DenseNet201. Empirical studies on the dataset collected from practical application demonstrates that the ED-HLOA-optimized DenseNet201 model achieves high accuracy in CHB image classification, verifying the effectiveness of the algorithm. Compared with several state-of-the-art optimization algorithms, ED-HLOA performs well on both the training and verification sets, effectively avoiding overfitting.

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Author Biography

Oifeng Su, Chengdu Technological University

College of Mathematics and System Sciences, Xinjiang University

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