Abstract
As on 15th September 2020, the total cases of SARS-CoV-2 infected patients in the world has crossed 29 million, with more than 930,000 deaths occurring due to the virus. Real time RT-PCR (Reverse Transcription Polymerase Chain Reaction), which is the standard detection method for COVID-19, is likely to have low detection rate in early stages of the infection possibly due to less viral load in the patients. On the other hand, in comparison to RT-PCR, patterns obtained from radiography on chest CT scans show higher sensitivity and specificity. However, due to the sensitive nature and difficulties in publicly acquiring medical data, only 2 open-sourced COVID CT datasets with images containing clinical findings of COVID-19 could be found. Applying existing deep learning models to the limited CT scans can distinguish COVID-19 and non-COVID-19 CT images but with lesser accuracy. The paper proposes to use existing deep learning-based frameworks on an augmented dataset consisting of pixel scaled images (of the original CT images) and the original CT images to diagnose COVID-19 infection. Since this is a binary classification problem the paper proposes to use Convolutional Neural Networks (CNN) to classify CT images into infected and not infected categories. The implementation is done with Keras and Tensorflow using an 80/20 ratio for training and validation. The proposed methodology (using pixel scaled images) achieved a validation accuracy of over 90% in detecting COVID-19 with an F1-score of 0.96 compared to the best F1-score of 0.86 on the original dataset.
Keywords
- COVID-19
- Chest CT scan
- Deep learning
- Convolutional neural network
- Data augmentation
- Pixel scaling
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Shetty, S., Gawade, A., Deolekar, S. (2022). Use of Deep Learning Based Frameworks on Pixel Scaled Images of Chest CT Scans for Detection of COVID-19. In: Nayak, J., Naik, B., Abraham, A. (eds) Understanding COVID-19: The Role of Computational Intelligence. Studies in Computational Intelligence, vol 963. Springer, Cham. https://doi.org/10.1007/978-3-030-74761-9_4
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