COVID-19 Patients Detection in Chest X-ray Images via MCFF-Net

Wen Wang, Yutao Li, Xin Wang, Ji Li, Peng Zhang
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引用次数: 1

Abstract

COVID-19 is a respiratory disease caused by severe acute respiratory syndrome coronavirus (SARS-CoV-2). This paper proposes a deep learning model to assist medical imaging physicians in diagnosing COVID-19 cases. We designed the Parallel Channel Attention Feature Fusion Module (PCAF), and brand new structure of convolutional neural network MCFF-Net was put forward. The experimental results show that the overall accuracy of MCFF-Net66-Conv1-GAP model is 96.79% for 3-class classification. Simultaneously, the precision, recall, specificity and the sensitivity for COVID-19 are both 100%. Compared with the latest state-of-art methods, the experimental results of our proposed method indicate its uniqueness.
MCFF-Net在胸部x线图像中检测COVID-19患者
COVID-19是一种由严重急性呼吸综合征冠状病毒(SARS-CoV-2)引起的呼吸道疾病。本文提出了一种深度学习模型来辅助医学影像医生诊断COVID-19病例。设计了并行通道注意力特征融合模块(PCAF),提出了全新的卷积神经网络结构MCFF-Net。实验结果表明,MCFF-Net66-Conv1-GAP模型对3类分类的总体准确率为96.79%。同时,对COVID-19的精密度、召回率、特异性和敏感性均为100%。实验结果表明,该方法与现有方法相比具有一定的独特性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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