D2-CovidNet: A Deep Learning Model for COVID-19 Detection in Chest X-Ray Images.
Wang XinYiyang HuYanhong LuoWang WeiPublished in: Computational intelligence and neuroscience (2021)
Since the outbreak of Coronavirus disease 2019 (COVID-19), it has been spreading rapidly worldwide and has not yet been effectively controlled. Many researchers are studying novel Coronavirus pneumonia from chest X-ray images. In order to improve the detection accuracy, two modules sensitive to feature information, dual-path multiscale feature fusion module and dense depthwise separable convolution module, are proposed. Based on these two modules, a lightweight convolutional neural network model, D2-CovidNet, is designed to assist experts in diagnosing COVID-19 by identifying chest X-ray images. D2-CovidNet is tested on two public data sets, and its classification accuracy, precision, sensitivity, specificity, and F 1-score are 94.56%, 95.14%, 94.02%, 96.61%, and 95.30%, respectively. Specifically, the precision, sensitivity, and specificity of the network for COVID-19 are 98.97%, 94.12%, and 99.84%, respectively. D2-CovidNet has fewer computation number and parameter number. Compared with other methods, D2-CovidNet can help diagnose COVID-19 more quickly and accurately.
Keyphrases
- coronavirus disease
- deep learning
- convolutional neural network
- sars cov
- artificial intelligence
- machine learning
- respiratory syndrome coronavirus
- high resolution
- big data
- computed tomography
- mental health
- optical coherence tomography
- mass spectrometry
- real time pcr
- loop mediated isothermal amplification
- drug induced
- acute respiratory distress syndrome