Convolutional Neural Networks with Transfer Learning for Recognition of COVID-19: A Comparative Study of Different Approaches
| Autor: | Mamta Garg, Om Prakash Mahela, Tanmay Garg, Akhil Ranjan Garg |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
principal component analysis
Computer science K-means clustering Feature selection 02 engineering and technology transfer learning Convolutional neural network lcsh:QA75.5-76.95 030218 nuclear medicine & medical imaging Domain (software engineering) Image (mathematics) 03 medical and health sciences 0302 clinical medicine convolutional neural networks 0202 electrical engineering electronic engineering information engineering General Environmental Science business.industry k-means clustering Pattern recognition Task (computing) Principal component analysis General Earth and Planetary Sciences 020201 artificial intelligence & image processing lcsh:Electronic computers. Computer science Artificial intelligence Transfer of learning business |
| Zdroj: | AI, Vol 1, Iss 34, Pp 586-606 (2020) AI Volume 1 Issue 4 Pages 34-606 |
| ISSN: | 2673-2688 |
| Popis: | To judge the ability of convolutional neural networks (CNNs) to effectively and efficiently transfer image representations learned on the ImageNet dataset to the task of recognizing COVID-19 in this work, we propose and analyze four approaches. For this purpose, we use VGG16, ResNetV2, InceptionResNetV2, DenseNet121, and MobileNetV2 CNN models pre-trained on ImageNet dataset to extract features from X-ray images of COVID and Non-COVID patients. Simulations study performed by us reveal that these pre-trained models have a different level of ability to transfer image representation. We find that in the approaches that we have proposed, if we use either ResNetV2 or DenseNet121 to extract features, then the performance of these approaches to detect COVID-19 is better. One of the important findings of our study is that the use of principal component analysis for feature selection improves efficiency. The approach using the fusion of features outperforms all the other approaches, and with this approach, we could achieve an accuracy of 0.94 for a three-class classification problem. This work will not only be useful for COVID-19 detection but also for any domain with small datasets. |
| Databáze: | OpenAIRE |
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