吉首大学学报（自然科学版）2026，Vol.47Issue(3)：41-49,9.DOI:10.13438/j.cnki.jdzk.2026.03.007
面向免疫固定电泳分类的输入增强双尺度网络模型
Input-Enhanced Dual-Scale Network for Immunofixation Electrophoresis Image Classification
符笑添 1谢剑宇 1谭子涵 1李曙2
作者信息
- 1. 吉首大学通信与电子工程学院,湖南 吉首 416000
- 2. 广州医科大学生物医学工程学院,广东 广州 511000
- 折叠
摘要
Abstract
To address the clinical challenges of immunofixation electrophoresis images,such as background fluctuations,noise interference,and faint bands leading to missed detections and misdiagnoses,we pro-pose a DGDB-IE2FNet model.The model features a dynamically gated dual-branch residual block and an IFE input enhancement dual-scale branch.The former adaptively fuses complementary convolutional branches,while the latter suppresses background noise and enhances directional information of bands.We formulate the classification of positive/negative results for ELP,G,A,M,K,and L lanes as a multi-label prediction task and conduct comparative experiments with several mainstream models using 20 000 real-world cases.Results show that the DGDB-IE2FNet achieves an accuracy of 99.63%,negative predictive value of 99.74%,sensitivity of 98.07%,and F1 score of 98.56%on the test set,outperforming other compared models in overall prediction performance.关键词
免疫固定电泳/深度学习/卷积神经网络/双尺度Key words
immunofixation electrophoresis/deep learning/convolutional neural networks/dual-scale分类
信息技术与安全科学引用本文复制引用
符笑添,谢剑宇,谭子涵,李曙..面向免疫固定电泳分类的输入增强双尺度网络模型[J].吉首大学学报（自然科学版）,2026,47(3):41-49,9.
