基于CNN与迁移学习实现肺部影像分类识别OA
Classification and Recognition of Lung Image Based on CNN and Transfer Learning
基于深度学习方法对肺部影像的智能分类识别做了创新性研究,提出了一种基于卷积神经网络和迁移学习的方法,选用了VGG、InceptionV3 和ResNet等经典CNN模型作为预训练模型,根据数据集的大小和相似性,选择了不同的迁移学习策略,文章还使用了数据增强、批量归一化和正则化等技术,提高了模型的泛化能力和稳定性.在COVID-19 CT scans、LIDC-IDRI两个公开的肺部影像数据集上进行了实验,实验结果证明了其有效性和鲁棒性,有助于提高诊断效率和准确度.
This paper presents an innovative research on intelligent classification and recognition of lung images based on Deep Learning methods,and proposes a method based on Convolutional Neural Networks(CNN)and Transfer Learning,which uses classic CNN models such as VGG,InceptionV3 and ResNet as pre-trained models,and selects different Transfer Learning strategies according to the size and similarity of the datasets.This paper also uses techniques such as data augm…查看全部>>
刘艺峰;罗亮
湖南工商大学,湖南 长沙 410205湖南工商大学,湖南 长沙 410205
计算机与自动化
卷积神经网络迁移学习肺部疾病
Convolutional Neural NetworksTransfer Learninglung disease
《现代信息科技》 2024 (7)
86-90,5
全国大学生创业训练计划目(202210554001X)
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