Convolutional neural network for prediction of COVID-19 from chest X-ray images
| Autor: | Abhishek Dey, Anwesha Law, Debasrita Chakraborty, Debayan Goswami |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Coronavirus disease 2019 (COVID-19)
Local binary patterns business.industry Computer science Pooling Feature extraction Process (computing) Pattern recognition Feature selection 010103 numerical & computational mathematics 02 engineering and technology 01 natural sciences Convolutional neural network Convolution 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence 0101 mathematics business |
| Zdroj: | CSBio '20: Proceedings of the Eleventh International Conference on Computational Systems-Biology and Bioinformatics. |
| DOI: | 10.1145/3429210.3429219 |
| Popis: | The COVID-19 pandemic has affected humans worldwide, and we are in dire need of techniques to bring this situation within our control. Among the various approaches attempted by researchers, preliminary prediction of COVID-19 through chest X-ray images is proving to be quite beneficial and thus, is being explored thoroughly. In this paper, a novel combination of local binary pattern based feature selection along with a convolutional neural network is proposed which can predict positive and negative cases by analysing chest X-ray images. The model consists of a feature extraction process followed by various pooling and convolution layers systematically placed to give an optimal output. The proposed model has been trained and tested on a COVID-19 CXR images dataset, and it is seen that it achieves a significant improvement over the five other comparison methods. |
| Databáze: | OpenAIRE |
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