COVID-19 Diagnosis via DenseNet and Optimization of Transfer Learning Setting

Yu Dong Zhang*, Suresh Chandra Satapathy, Xin Zhang, Shui Hua Wang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

48 Citations (Scopus)


COVID-19 is an ongoing pandemic disease. To make more accurate diagnosis on COVID-19 than existing approaches, this paper proposed a novel method combining DenseNet and optimization of transfer learning setting (OTLS) strategy. Preprocessing was used to enhance, crop, and resize the collected chest CT images. Data augmentation method was used to increase the size of training set. A composite learning factor (CLF) was employed which assigned different learning factor to three types of layers: frozen layers, middle layers, and new layers. Meanwhile, the OTLS strategy was proposed. Finally, precomputation method was utilized to reduce RAM storage and accelerate the algorithm. We observed that optimization setting “201-IV” can achieve the best performance by proposed OTLS strategy. The sensitivity, specificity, precision, and accuracy of our proposed method were 96.35 ± 1.07, 96.25 ± 1.16, 96.29 ± 1.11, and 96.30 ± 0.56, respectively. The proposed DenseNet-OTLS method achieved better performances than state-of-the-art approaches in diagnosing COVID-19.

Original languageEnglish
JournalCognitive Computation
Publication statusAccepted/In press - 2021
Externally publishedYes


  • COVID-19
  • Composite learning factor
  • Data augmentation
  • DenseNet
  • Optimization
  • Precomputation
  • Transfer learning


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