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https://github.com/Jafar-Abdollahi/Automated-detection-of-COVID-19-cases-using-deep-neural-networks-with-CTS-images

The use of advanced artificial intelligence (AI) techniques combined with radiological imaging can be useful for accurate diagnosis of the disease and can also help overcome the shortage of specialist physicians in remote villages. In this project, a new model for automatic detection of covid-19 using raw chest X-ray images is presented. The proposed model is designed to provide an accurate diagnosis for binary classification (COVID vs. pneumonia ) and multi-classification (covid, pneumonia, nodel, boronshit, normal). Our model produces 99.08% classification accuracy for binary classifications and 95.02% for multi-class cases. The DarkNet model was used in our study as a classification where you only look at the real-time object recognition system once (YOLO(v3)). We applied 17 layers of the convolution and applied different filters on each layer. Our model can be used to help radiologists discredit their initial screening and can also be used over the cloud for rapid screening of patients.
https://github.com/Jafar-Abdollahi/Automated-detection-of-COVID-19-cases-using-deep-neural-networks-with-CTS-images

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The use of advanced artificial intelligence (AI) techniques combined with radiological imaging can be useful for accurate diagnosis of the disease and can also help overcome the shortage of specialist physicians in remote villages. In this project, a new model for automatic detection of covid-19 using raw chest X-ray images is presented. The proposed model is designed to provide an accurate diagnosis for binary classification (COVID vs. pneumonia ) and multi-classification (covid, pneumonia, nodel, boronshit, normal). Our model produces 99.08% classification accuracy for binary classifications and 95.02% for multi-class cases. The DarkNet model was used in our study as a classification where you only look at the real-time object recognition system once (YOLO(v3)). We applied 17 layers of the convolution and applied different filters on each layer. Our model can be used to help radiologists discredit their initial screening and can also be used over the cloud for rapid screening of patients.

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