基于CNN与Transformer混合模型的肺炎辅助诊断  

Pneumonia Assisted Diagnosis Based on Hybrid Model of CNN and Transformer

作　　者：贠恺 贾荣浩 魏国辉 赵爽 李学辉 马志庆[1] YUN Kai;JIA Rong-Hao;WEI Guo-Hui;ZHAO Shuang;LI Xue-Hui;MA Zhi-Qing(School of Intelligence and Information Engineering,Shandong University of Traditional Chinese Medicine,Jinan 250355,China;Laboratory Management Office,Shandong University of Traditional Chinese Medicine,Jinan 250355,China)

机构地区：[1]山东中医药大学智能与信息工程学院,济南250355 [2]山东中医药大学实验室管理处,济南250355

出　　处：《计算机系统应用》2025年第2期216-224,共9页Computer Systems & Applications

基　　金：国家自然科学基金(61702087);山东省研究生教育质量提升计划(SDYJG1943);山东中医药大学科学研究基金(KYZK2024Q30)。

摘　　要：肺炎是一种常见的呼吸系统疾病,早期诊断对于有效治疗至关重要.本研究提出了卷积神经网络(CNN)和Transformer结合的CTFNet混合模型,旨在实现高效而准确的肺炎辅助诊断.该模型融合了卷积分词器和聚焦线性注意力机制.卷积分词器通过卷积操作实现更紧凑的特征提取,并保留图像的关键局部特征降低计算复杂度,提高模型的表达能力.聚焦线性注意力机制缓解了Transformer的计算需求,优化了注意力框架,大幅提升了模型性能.在Chest X-ray Images数据集上,CTFNet在肺炎分类任务中表现出色,达到了99.32%的准确率、99.55%的精确率、99.55%的召回率和99.55%的F1值.较好的性能凸显了该模型在临床应用中的潜力.为了评估CTFNet的泛化能力,我们将其应用于COVID-19 Radiography Database数据集.在该数据集中,CTFNet被用于多个二分类任务均达到98%以上的准确率.这些结果表明,CTFNet在肺炎图像分类的各种任务中具有较好的泛化能力和可靠性.Pneumonia is a prevalent respiratory disease for which early diagnosis is crucial to effective treatment.This study proposes a hybrid model,CTFNet,which combines convolutional neural network(CNN)and Transformer to aid in the effective and accurate diagnosis of pneumonia.The model integrates a convolutional tokenizer and a focused linear attention mechanism.The convolutional tokenizer performs more compact feature extraction through convolution operations,retaining key local features of images while reducing computational complexity to enhance model expressiveness.The focused linear attention mechanism reduces the computational demands of the Transformer and optimizes the attention framework,significantly improving model performance.On the Chest X-ray Images dataset,CTFNet demonstrates outstanding performance in pneumonia classification tasks,achieving an accuracy of 99.32%,a precision of 99.55%,a recall of 99.55%,and an F1-score of 99.55%.The impressive performance highlights the model’s potential for clinical applications.The model is evaluated on the COVID-19 Radiography Database dataset for its generalization ability.In this dataset,CTFNet achieves an accuracy above 98%in multiple binary classification tasks.These results indicate that CTFNet exhibits strong generalization ability and reliability across various tasks in pneumonia image classification.

关 键 词：肺炎图像分类 卷积神经网络 TRANSFORMER 卷积分词器 聚焦线性注意力机制 

分 类 号：R563.1[医药卫生—呼吸系统] TP183[医药卫生—内科学] TP391.41[医药卫生—临床医学]