CXR Image Classification Based on Residual Convolution and Multi-headed Self-attentions

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- CXR Image Classification Based on Residual Convolution and Multi-headed Self-attentions
- “在提高COVID-19检测效率和准确性方面取得新进展，研究者开发了MHRA-RCNet模型，通过局部特征提取和全局信息建模，显著提升了胸部X射线图像分类的精度和有效性。”
- Advanced Engineering Sciences Vol. 56, Issue 3, Pages: 219-227(2024)
- 作者机构：
  
  1.安徽理工大学计算机科学与工程学院，安徽淮南 232001
  2.安徽三联学院智慧交通现代产业学院，安徽合肥 230601
- 作者简介：
- 基金信息：
- DOI：10.12454/j.jsuese.202200895
  CLC： TP391.4
- Published：20 May 2024，
  
  Published Online：06 May 2024，
  
  Received：25 August 2022，
  
  Revised：09 March 2023，
- 稿件说明：
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陈辉,张甜,陈润斌.基于残差卷积与多头自注意力的CXR图像分类[J].工程科学与技术,2024,56(3):219–227

Chen Hui,Zhang Tian,Chen Runbin.CXR image classification based on residual convolution and multi-headed self-attentions[J].Advanced Engineering Sciences,2024,56(3):219–227
陈辉,张甜,陈润斌.基于残差卷积与多头自注意力的CXR图像分类[J].工程科学与技术,2024,56(3):219–227 DOI： 10.12454/j.jsuese.202200895.

Chen Hui,Zhang Tian,Chen Runbin.CXR image classification based on residual convolution and multi-headed self-attentions[J].Advanced Engineering Sciences,2024,56(3):219–227 DOI： 10.12454/j.jsuese.202200895.

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