Back to Search
Start Over
COVID-19 early detection for imbalanced or low number of data using a regularized cost-sensitive CapsNet
- Source :
- Scientific Reports, Scientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
- Publication Year :
- 2021
- Publisher :
- Springer Science and Business Media LLC, 2021.
-
Abstract
- With the presence of novel coronavirus disease at the end of 2019, several approaches were proposed to help physicians detect the disease, such as using deep learning to recognize lung involvement based on the pattern of pneumonia. These approaches rely on analyzing the CT images and exploring the COVID-19 pathologies in the lung. Most of the successful methods are based on the deep learning technique, which is state-of-the-art. Nevertheless, the big drawback of the deep approaches is their need for many samples, which is not always possible. This work proposes a combined deep architecture that benefits both employed architectures of DenseNet and CapsNet. To more generalize the deep model, we propose a regularization term with much fewer parameters. The network convergence significantly improved, especially when the number of training data is small. We also propose a novel Cost-sensitive loss function for imbalanced data that makes our model feasible for the condition with a limited number of positive data. Our novelties make our approach more intelligent and potent in real-world situations with imbalanced data, popular in hospitals. We analyzed our approach on two publicly available datasets, HUST and COVID-CT, with different protocols. In the first protocol of HUST, we followed the original paper setup and outperformed it. With the second protocol of HUST, we show our approach superiority concerning imbalanced data. Finally, with three different validations of the COVID-CT, we provide evaluations in the presence of a low number of data along with a comparison with state-of-the-art.
- Subjects :
- Coronavirus disease 2019 (COVID-19)
Computer science
Science
media_common.quotation_subject
Early detection
Machine learning
computer.software_genre
Regularization (mathematics)
Article
Deep Learning
Humans
Function (engineering)
Computed tomography
Lung
media_common
Protocol (science)
Multidisciplinary
business.industry
Deep learning
COVID-19
Term (time)
Early Diagnosis
Medicine
Radiographic Image Interpretation, Computer-Assisted
Neural Networks, Computer
Artificial intelligence
Tomography, X-Ray Computed
business
computer
Algorithms
Drawback
Subjects
Details
- ISSN :
- 20452322
- Volume :
- 11
- Database :
- OpenAIRE
- Journal :
- Scientific Reports
- Accession number :
- edsair.doi.dedup.....90360b476464d59576fa347d3bffb39c