Detection of COVID-19 using Deep Learning with CT Scan Images

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Triratana Metkarunchit
Kiarttipum Charoenpojvajana

Abstract

   The demand of testing the potential infected patient of “new corona virus” or COVID-19 has been enormously increased as the virus is still continuously and immensely spread in many countries. The popular method of testing is to analyze the genetic material of the virus with reverse transcription - polymerase chain reaction (RT-PCR). Recently, the chest x-rays have been introduced to diagnose the infection as it is considered to be easier and crucial method in this circumstance, especially when combine with a deep learning that can recognize and detect the abnormality of lung parenchyma, in which considered to be the signature of COVID-19 effectively. The purpose of this article is to adapt the mask region-based convolutional neural networks (Mask RCNN) to segment the regions that had been affected by the virus by using computed tomography (CT) scan on the chest. The prediction of the tissues regions that have been damaged will help the medical team to classify if the patients are in ‘mild’ or ‘danger’ situation easier. From the model test result processed on google cloud platform, this model F1 scores equivalent to 89% and has the average of speed inference at 9.71 second.

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References

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