Generalized Deep Learning Models for COVID-19 Detection with Transfer and Continual Learning

Richard Annan; Hong Qin; Letu Qingge

doi:10.29007/952w

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Generalized Deep Learning Models for COVID-19 Detection with Transfer and Continual Learning

15 pages•Published: July 12, 2024

Richard Annan, Hong Qin and Letu Qingge

Abstract

Deep learning has achieved great success for detecting COVID-19 from CT scan images. However, there is lack of generalization ability for the existing models. For example, one model with a higher prediction accuracy developed on one dataset cannot be used to pre- dict on another dataset. Thus, developing a robust deep learning model that has a great generalization ability is a significant need. In this paper, we first apply three deep learning models, namely convolutional neural network (CNN), capsule neural network (CapsNet) and vision transformer (ViT) and test their generalization abilities. Then, we develop and hypertune the models based on transfer learning to generalize the model performance on new datasets. However, the transfer learning technique always has the catastrophic forgetting issue which lead to lower prediction accuracy on its original training dataset. Lastly, we will apply continual learning based on modified elastic weight consolidation (EWC) regularization technique to address the catastrophic forgetting issue and improve the models’ prediction accuracy on both new and original training datasets. Our results on cross-data validation show that our proposed models not only achieve better prediction accuracy of up to 97.85% compared with the existing state-of-the-art models, but also the proposed models with EWC show great generalization ability and retain the higher prediction accuracy on both new dataset and the training dataset. Extensive experiments show that our proposed COVID-CNN model with EWC outperforms ViT and CapsNet with an impressive 82.26% knowledge retention rate on the original training dataset. Our developed code can be found from https://github.com/astonish24/-QinggeLab BICOB24.

Keyphrases: Continual Learning, COVID-19 detection, deep learning, Transfer Learning

In: Hisham Al-Mubaid, Tamer Aldwairi and Oliver Eulenstein (editors). Proceedings of the 16th International Conference on Bioinformatics and Computational Biology (BICOB-2024), vol 101, pages 58--72

Links:	https://easychair.org/publications/paper/FzLP
	https://doi.org/10.29007/952w

BibTeX entry

@inproceedings{BICOB-2024:Generalized_Deep_Learning_Models,
  author    = {Richard Annan and Hong Qin and Letu Qingge},
  title     = {Generalized Deep Learning Models for COVID-19 Detection with Transfer and Continual Learning},
  booktitle = {Proceedings of the 16th International Conference on Bioinformatics and Computational Biology (BICOB-2024)},
  editor    = {Hisham Al-Mubaid and Tamer Aldwairi and Oliver Eulenstein},
  series    = {EPiC Series in Computing},
  volume    = {101},
  pages     = {58--72},
  year      = {2024},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {https://easychair.org/publications/paper/FzLP},
  doi       = {10.29007/952w}}

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