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A comparative analysis of eleven neural networks architectures for small datasets of lung images of COVID-19 patients toward improved clinical decisions

  • Yang, Yuan1, 2
  • Zhang, Lin1, 2
  • Du, Mingyu1, 2
  • Bo, Jingyu3
  • Liu, Haolei1, 2
  • Ren, Lei1, 2
  • Li, Xiaohe4
  • Deen, M.Jamal2, 5
  • 1 Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, No.37 Xueyuan Road, Haidian District, Beijing, China
  • 2 Key Laboratory of Big Data-Based Precision Medicine,Ministry of Industry and Information Technology, No.37 Xueyuan Road, Haidian District, Beijing, China
  • 3 School of Economics and Management, Beijing Jiaotong University, No.3, Shangyuan Village, Haidian District, Beijing, China
  • 4 The Third People’s Hospital of Shenzhen, Shenzhen, China
  • 5 Department of Electrical Ad Computer Engineering, McMaster University, Hamilton, ON L8S 4K1, Canada
Published Article
Computers in Biology and Medicine
Published by Elsevier Ltd.
Publication Date
Sep 24, 2021
DOI: 10.1016/j.compbiomed.2021.104887
PMCID: PMC8461289
PubMed Central
  • Article


The 2019 novel severe acute respiratory syndrome coronavirus 2-SARS-CoV2, commonly known as COVID-19, is a highly infectious disease that has endangered the health of many people around the world. COVID-19, which infects the lungs, is often diagnosed and managed using X-ray or computed tomography (CT) images. For such images, rapid and accurate classification and diagnosis can be performed using deep learning methods that are trained using existing neural network models. However, at present, there is no standardized method or uniform evaluation metric for image classification, which makes it difficult to compare the strengths and weaknesses of different neural network models. This paper used eleven well-known convolutional neural networks, including VGG-16, ResNet-18, ResNet-50, DenseNet-121, DenseNet-169, Inception-v3, Inception-v4, SqueezeNet, MobileNet, ShuffeNet, and EfficientNet-b0, to classify and distinguish COVID-19 and non-COVID-19 lung images. These eleven models were applied to different batch sizes and epoch cases, and their overall performance was compared and discussed. The results of this study can provide decision support in guiding research on processing and analyzing small medical datasets to understand which model choices can yield better outcomes in lung image classification, diagnosis, disease management and patient care.

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