内蒙古民族大学学报（自然科学版）2025，Vol.40Issue(1)：22-27,6.DOI:10.14045/j.cnki.15-1220.2025.01.004
基于迁移学习的改进EfficientNet网络的皮肤病分类研究
Research on Skin Lesion Classification Using the Improved EfficientNet Network Based on Transfer Learning
摘要
Abstract
In view of the problems of large network model parameters and low classification accuracy in current skin disease auxiliary classification technology,an improved EfficientNet skin disease classification method based on transfer learning is proposed.This method applies the idea of transfer learning to improve the lightweight deep convolutional neural network EfficientNet,including adding global average pooling layers,freezing different layers and fine-tuning the model to form TL-EfficientNet network.The experimental results show that the accuracy of TL-EfficientNetB0 on the ISIC2018 skin lesion dataset after class weight preprocessing reaches 85.07%,Macro_P reach-es 0.82,and the number of the network parameters is only 4.49 M,which is suitable for mobile deployment.关键词
迁移学习/轻量级卷积神经网络/EfficientNet/皮肤病分类Key words
transfer learning/lightweight convolutional neural network/EfficientNet/skin lesion classification分类
信息技术与安全科学引用本文复制引用
赵海燕,乌有腾,任梦晗..基于迁移学习的改进EfficientNet网络的皮肤病分类研究[J].内蒙古民族大学学报（自然科学版）,2025,40(1):22-27,6.基金项目
留学人员创新创业启动支持计划项目(2024LXCX003) （2024LXCX003）
