A Seven-Layer Convolutional Neural Network for Chest CT-Based COVID-19 Diagnosis Using Stochastic Pooling

Yudong Zhang, Suresh Chandra Satapathy, Li Yao Zhu*, Juan Manuel Górriz*, Shuihua Wang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

55 Citations (Scopus)

Abstract

(Aim) COVID-19 pandemic causes numerous death tolls till now. Chest CT is an effective imaging sensor system to make accurate diagnosis. (Method) This article proposed a novel seven layer convolutional neural network based smart diagnosis model for COVID-19 diagnosis (7L-CNN-CD). We proposed a 14-way data augmentation to enhance the training set, and introduced stochastic pooling to replace traditional pooling methods. (Results) The 10 runs of 10-fold cross validation experiment show that our 7L-CNN-CD approach achieves a sensitivity of 94.44±0.73, a specificity of 93.63±1.60, and an accuracy of 94.03±0.80. (Conclusion) Our proposed 7L-CNN-CD is effective in diagnosing COVID-19 in chest CT images. It gives better performance than several state-of-the-art algorithms. The data augmentation and stochastic pooling methods are proven to be effective.

Original languageEnglish
Pages (from-to)17573-17582
Number of pages10
JournalIEEE Sensors Journal
Volume22
Issue number18
DOIs
Publication statusPublished - 15 Sept 2022
Externally publishedYes

Keywords

  • Deep learning
  • convolutional neural network
  • data augmentation
  • stochastic pooling; COVID-19

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