福建电脑2024，Vol.40Issue(7)：33-36,4.DOI:10.16707/j.cnki.fjpc.2024.07.006
深度学习在肺炎CT图像分类识别中的应用研究
Research on the Application of Deep Learning in Pneumonia CT Image Classification and Recognition
摘要
Abstract
In order to improve the accuracy and efficiency of CT image classification and recognition for pneumonia,this study used the network model DenseNet-121 to classify and recognize CT images.Select a publicly available CT image dataset for pneumonia,first perform data preprocessing,then use the network architecture to extract image features,and optimize model performance through transfer learning.The experimental results show that the model achieves high accuracy in the recognition of normal and pneumonia,improving classification efficiency.关键词
肺炎/深度学习/图像识别与分类/DenseNet-121模型Key words
Pneumonia/Deep Learning/Image Classification and Recognition/DenseNet-121 Model分类
信息技术与安全科学引用本文复制引用
王伟德,宛楠,王鑫,张骏,储佳乐,方超..深度学习在肺炎CT图像分类识别中的应用研究[J].福建电脑,2024,40(7):33-36,4.基金项目
本文得到安徽省省级大学生创新创业训练计划项目(No.S202210368077)、2022年皖南医学院教学质量与教学改革工程项目(No.2022xsxxkc03)、2022年安徽省质量工程项目(No.2022shsj025)资助. （No.S202210368077）
