COV-SNET: A deep learning model for X-ray-based COVID-19 classification

Informatics in Medicine Unlocked

Rachid Benlamri¹  Robert Hertel² 

[1] Lakehead University, 955 Oliver Rd, Thunder Bay, ON P7B 5E1, Canada;Corresponding author.;

关键词: Coronavirus;    COVID-19;    Convolutional neural network;    Deep learning;    Chest X-ray;    Computer vision;   

DOI  :  

来源: DOAJ

【 摘 要 】

The AI research community has recently been intensely focused on diagnosing COVID-19 by applying deep learning technology to the X-ray scans taken of COVID-19 patients. Differentiating COVID-19 from other pneumonia-inducing illnesses is a highly challenging task as it shares many of the same imaging characteristics as other pulmonary diseases. This is especially true given the small number of COVID-19 X-rays that are publicly available. Deep learning experts commonly use transfer learning to offset the small number of images typically available in medical imaging tasks. Our COV-SNET model is a deep neural network that was pretrained on over one hundred thousand X-ray images. In this paper, we designed two COV-SNET models with the purpose of diagnosing COVID-19. The experimental results demonstrate the robustness of our deep learning models, ultimately achieving sensitivities of 95% for our three-class and two-class models. We also discuss the strengths and weaknesses of such an approach, focusing mainly on the limitations of public X-ray datasets on current COVID-19 deep learning models. Finally, we conclude with possible future directions for this research.

【 授权许可】

Unknown