| Autor: |
Sai Kishore H R, M S Bhargavi, Pavan Kumar C |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
2021 IEEE Region 10 Symposium (TENSYMP). |
| DOI: |
10.1109/tensymp52854.2021.9550878 |
| Popis: |
The Coronavirus disease is a respiratory infection caused by the Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2), also known colloquially as COVID-19 virus. Non-Covid Viral Pneumonia is also a respiratory disease which affects the lungs of the patients. It becomes difficult for radiologists and pulmonologists to differentiate between these two respiratory diseases. Chest X-ray images of the patients can be used to efficiently diagnose between these two respiratory diseases. Deep learning models can be efficiently used to detect subtle differences between these X-ray images. This method can be used to gain faster results in COVID-19 cases thereby reducing the time taken for identifying a COVID-19 patient. X- ray images are cheaper and faster than the currently existing methods. A Transfer Learning approach is adopted to classify chest X-rays into three categories such as COVID-19, Pneumonia and Normal. Popular ImageNet Architectures: VGG-19, MobileNet and ResNet-50 are used to classify X-ray images of the patients. From the experimental results, it is evident that the MobileNet is able to achieve a validation accuracy of 0.9777 in 40 epochs. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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