DDCNNC: Dilated and depthwise separable convolutional neural Network for diagnosis COVID-19 via chest X-ray images
| Autor: | Mengyao Zhai, Xiang Li, Junding Sun |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Information Systems and Management
Coronavirus disease 2019 (COVID-19) Computer science business.industry Generalization Deep learning Training time COVID-19 Pattern recognition Depthwise separable convolution Convolutional neural network Article Computer Science Applications Separable space Robustness (computer science) X ray image Convolutional neural networks Artificial intelligence business Engineering (miscellaneous) Information Systems Dilated convolution |
| Zdroj: | International Journal of Cognitive Computing in Engineering |
| ISSN: | 2666-3074 |
| Popis: | Purpose As of December 21, 2020, a total of 77,670,400 cases of coronavirus disease 2019 (COVID-19) have been confirmed worldwide, 53,825,243 cases have been cured and 1,693,253 cases have died. Among the diagnostic methods of COVID-19, chest X-ray images have the advantages of fast imaging, low cost and high accuracy of single plane lesions recognition. The current COVID-19 detection models have shortcomings such as weak robustness, unreliable generalization ability, and long training time. Methods To solve the above problems, our team proposed two novel frameworks and five methods to diagnose COVID-19 based on chest X-ray images. (i) A novel framework – depthwise separable convolutional neural network (DCNN), and we tested Three methods, viz., using LeNet-5, VGG-16, and ResNet-18 as backbones. (ii) A novel framework – dilated and depthwise separable convolutional neural network (DDCNN), and we tested Two methods, viz., using VGG-16 and ResNet-18 as backbones. Results Experiment results show that our models not only improve the detection accuracy, but also reduce the training time. Conclusions Our methods are superior to state-of-the-art methods in both above aspects. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|