TeliNet, a simple and shallow Convolution Neural Network (CNN) to Classify CT Scans of COVID-19 patients

• URL: http://arxiv.org/abs/2107.04930v1
• Date: Sat, 10 Jul 2021 23:46:14 GMT
• Title: TeliNet, a simple and shallow Convolution Neural Network (CNN) to Classify CT Scans of COVID-19 patients
• Authors: Mohammad Nayeem Teli
• Abstract summary: Hundreds of millions of cases and millions of deaths have occurred worldwide due to COVID-19. In this research we present a simple and shallow Convolutional Neural Network based approach, TeliNet, to classify CT-scan images of COVID-19 patients.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Hundreds of millions of cases and millions of deaths have occurred worldwide due to COVID-19. The fight against this pandemic is on-going on multiple fronts. While vaccinations are picking up speed, there are still billions of unvaccinated people. In this fight diagnosis of the disease and isolation of the patients to prevent any spreads play a huge role. Machine Learning approaches have assisted the diagnosis of COVID-19 cases by analyzing chest X-ray and CT-scan images of patients. In this research we present a simple and shallow Convolutional Neural Network based approach, TeliNet, to classify CT-scan images of COVID-19 patients. Our results outperform the F1 score of VGGNet and the benchmark approaches. Our proposed solution is also more lightweight in comparison to the other methods.

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