LCCNN: a Lightweight Customized CNN-Based Distance Education App for COVID-19 Recognition

Jiaji Wang, Suresh Chandra Satapathy, Shuihua Wang*, Yudong Zhang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


In the global epidemic, distance learning occupies an increasingly important place in teaching and learning because of its great potential. This paper proposes a web-based app that includes a proposed 8-layered lightweight, customized convolutional neural network (LCCNN) for COVID-19 recognition. Five-channel data augmentation is proposed and used to help the model avoid overfitting. The LCCNN achieves an accuracy of 91.78%, which is higher than the other eight state-of-the-art methods. The results show that this web-based app provides a valuable diagnostic perspective on the patients and is an excellent way to facilitate medical education. Our LCCNN model is explainable for both radiologists and distance education users. Heat maps are generated where the lesions are clearly spotted. The LCCNN can detect from CT images the presence of lesions caused by COVID-19. This web-based app has a clear and simple interface, which is easy to use. With the help of this app, teachers can provide distance education and guide students clearly to understand the damage caused by COVID-19, which can increase interaction with students and stimulate their interest in learning.

Original languageEnglish
JournalMobile Networks and Applications
Publication statusAccepted/In press - 2023
Externally publishedYes


  • COVID-19
  • Convolutional neural network
  • Deep learning
  • Distance education
  • Medical image diagnosis
  • Web app


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