Detecting Covid-19 in Chest X-Rays using Transfer Learning with VGG16
| Autor: | Hassaan Inayatali, Jonathan H. Chan, Nipon Charoenkitkarn, Amy Chen, Jonathan Jaegerman, Dunja Matic |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
medicine.diagnostic_test
Coronavirus disease 2019 (COVID-19) business.industry Computer science Class activation mapping Computed tomography Machine learning computer.software_genre 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine 030220 oncology & carcinogenesis medicine Screening method Artificial intelligence Medical diagnosis business Transfer of learning computer |
| Zdroj: | CSBio '20: Proceedings of the Eleventh International Conference on Computational Systems-Biology and Bioinformatics. |
| DOI: | 10.1145/3429210.3429213 |
| Popis: | Covid-19 is a novel epidemic that has hugely impacted countries worldwide [13]; and for which there is a need for quick and accurate screening methods. Current testing methods include the reverse transcription-polymerase chain reaction test and medical diagnosis using computed tomography scans. Both of these require expensive technologies as well as highly-trained practitioners and thus are in short supply [18]. Less developed countries and overloaded hospitals have increased the demand for cheap, easy and accurate screening methods [4]. X-ray devices are now cheap, portable and easy to use; there are few professionals, however, who are capable of manually identifying Covid-19 from a chest x-ray. We suggest implementing a machine learning model that incorporates transfer learning to automatically detect Covid-19 from chest x-ray images. The suggested model is built on top of the VGG16 architecture and pre-trained ImageNet weights. Compared with the VGG19, Inception-V3, Inception-ResNet, Xception, RestNet152-V2, and DenseNet201 models, the VGG16 model achieved the highest testing accuracy of 98% on 10 epochs as well as high positive-class accuracy. Gradient-weighted class activation mapping (Grad-CAM) was also applied to detect the regions that have a greater impact on the model classification decision. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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