基于改进EfficientNet的轻量型白细胞图像识别模型  

A lightweight white blood cells image recognition model based on improved EfficientNet

作　　者：刘欢 吴亮红[1] 陈亮 周博文[1] LIU Huan;WU Liang-hong;CHEN Liang;ZHOU Bo-wen(School of Information and Electrical Engineering,Hunan University of Science and Technology,Xiangtan 411201,China)

机构地区：[1]湖南科技大学信息与电气工程学院,湖南湘潭411201

出　　处：《计算机工程与科学》2023年第12期2206-2215,共10页Computer Engineering & Science

基　　金：国防基础科研计划(JCKY2019403D006);湖南省科技创新计划(2017XK2302);湖南省自然科学基金(2021JJ30280,2022JJ30265);湖南省科技人才托举工程项目中青年学者培养计划(2022TJ-Q03)。

摘　　要：针对白细胞识别模型的部署受到参数数量和计算的限制,导致白细胞识别准确率较低和模型泛化能力较差等问题,提出了一种基于改进EfficientNet的轻量高效的白细胞图像识别模型。首先,减少主要模块堆叠减少模型参数量,同时添加特征层间的跳跃连接保证信息的传递;其次,用改进的有效通道注意力和DropBlock2D对主要模块进行调整,使模型捕获更多通道和细节的特征信息,以提升模型的准确率和泛化能力;最后,使用带有标签平滑的交叉熵损失函数对模型进行训练,加快模型的收敛,以进一步提高模型的泛化能力。实验结果表明,改进后模型的参数量为2.49 M,较改进前减少了1.11 M,降低了模型复杂度,在混合数据集上达到了99.67%的准确率,较改进前提高了0.37%,在公共数据集BCCD2上达到了100%的准确率,高于现有的白细胞识别模型的准确率,验证了该模型在保持轻量级计算的基础上,具有较高的准确率和良好的泛化能力。Most white blood cells(WBCs)recognition models present the disadvantages such as limited deployment due to large parameter count and computation amount,low WBC recognition accuracy,and poor generalization ability.Therefore,a lightweight and efficient WBCs recognition model based on improved EfficientNet is proposed.Firstly,the main modules are streamlined to reduce the model parameter count,while jump connections between feature layers are added to ensure a complete information flow.Secondly,the main module is optimized by adding the improved efficient channel attention and selecting a more suitable DropBlock2D.The improved module makes the model capture more channels and detail features,thus improving the recognition accuracy and generalization ability.Finally,the model is trained by a cross-entropy loss function with label smoothing to accelerate the convergence of the model and further enhance the generalization ability further.The experimental results show that the number of parameters of the improved model is 2.49M,which is 1.11M less than that before the improvement,simplifying the complexity of the model.The improved model achieves 99.67%accuracy in the classification task for the mixed dataset,which is 0.37%better than before the improvement.In addition,the model achieves 100.00%accuracy in the classification of the public dataset BCCD2,which is higher than the existing WBCs recognition models,verifying that the model has high accuracy and good generalization ability while maintaining lightweight computation.

关 键 词：白细胞识别 EfficientNet 通道注意力 DropBlock2D 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]