TeliNet: Classifying CT Scan Images for COVID-19 Diagnosis

Mohammad Nayeem Teli; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, 2021, pp. 496-502


COVID-19 has led to hundreds of millions of cases and millions of deaths worldwide since its onset. 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 against the virus, di- agnosis of the disease and isolation of the patients to pre- vent any spread play a huge role. Machine Learning ap- proaches have assisted in the diagnosis of COVID-19 cases by analyzing chest X-rays and CT-scan images of patients. To push algorithm development and research in this direc- tion of radiological diagnosis, a challenge to classify CT- scan series was organized in conjunction with ICCV, 2021. In this research we present a simple and shallow Convo- lutional Neural Network based approach, TeliNet, to clas- sify these CT-scan images of COVID-19 patients presented as part of this competition. Our results outperform the F1 'macro' score of the competition benchmark and VGGNet approaches. Our proposed solution is also more lightweight in comparison to the other methods.

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@InProceedings{Teli_2021_ICCV, author = {Teli, Mohammad Nayeem}, title = {TeliNet: Classifying CT Scan Images for COVID-19 Diagnosis}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops}, month = {October}, year = {2021}, pages = {496-502} }